Paper
Library.

Nontechnical users need scalable control across email, files, payments, and personal data. Per-action approval is interruptive, while fully automated review may not represent personal preferences. Reusable allow/ask/never rules appear to offer a middle path.

A language model maps actions into four consequence categories. The study assigns 113 participants without professional software backgrounds to per-action human review, automated per-action model review, or user-authored consequence policies. Participants supervise an 18-action simulated day containing seven overreach actions.

Policies blocked 20.1 percentage points less overreach than human review and 14.5 points less than automated review. Runtime prompts fell from 18.0 to 10.9, but total intervention time was not reliably lower after policy setup. Participants chose “ask” for 114 of 140 rules; 133 of 148 executed overreach actions followed explicit human approval.

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Real review is iterative. A defect may be introduced, partially fixed, reopened, or obscured by later changes. Existing evaluations often collapse this lifecycle into one classification, hiding temporal and memory failures.

MCR-Bench collects 2,269 real multi-round review tasks across five programming languages, annotating defect description, type, severity, and cross-round state. Mainstream models are tested on defect detection and lifecycle tracking, with error analysis by defect class, salience, severity, and interaction depth.

Evaluated models show limited overall performance and degrade as review rounds increase. Semantically complex and low-salience defects are missed more often. False positives and false negatives reflect cross-round temporal misalignment and inadequate long-range memory rather than only weak code understanding.

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Agents often stop because a model believes the task is done. Existing critics may see the trace yet still accept unsupported or premature completion, especially after tool faults, stale values, or missing receipts.

Evidence-Carrying Termination binds each answer claim to valid in-scope trace evidence and runs a closed deterministic replay. A locked 48-task synthetic study crosses six tool families and eight fault types; a fresh 576-trajectory study compares ECT with a critic core, faithful controller, and full-trace model critic on held-out task clusters.

ECT produced 0/288 unsafe completions versus 252/288 for the inspected critic core in the locked study. On 22 held-out clusters it produced 0/66 premature unsupported terminations versus 40/66 for the controller, while supported completion was 97/132 versus 92/132 and met a prespecified -10-point noninferiority margin. Seventeen of 18 recovery trajectories later completed with support.

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Prompt-injection defenses often protect one plan or one tool call. A persistent agent can store attacker-controlled content, reuse it later, allow it to influence branching, or launder it into privileged arguments across sessions.

SPA invokes a planner once per query to produce a declarative executable plan. Dual-lattice information-flow control tracks confidentiality and integrity through explicit data and control dependencies. Results persist as labeled artifacts; later planning sees semantic metadata rather than raw untrusted payloads. Evaluation uses AgentDojo and a new multi-query extension.

Under the named tool knowledge attack, SPA with information-flow control reports zero attack success on AgentDojo and 0.2% on AgentDojo-MQ. The authors also identify reduced utility under strict integrity enforcement.

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KV cache dominates memory in long-context inference. Many eviction methods rank tokens by attention or distance to a global reference, assuming those signals track importance. The wrong eviction silently destroys needed evidence.

A leave-one-out probe tests the link between attention and causal contribution. TwinKV then finds near-duplicate keys, identifies evicted keys with no surviving duplicate and retained keys whose information is duplicated, and swaps them under the same budget. It composes with four policies across LongBench, LooGLE, RULER, and an MMLU-Pro no-harm control at 0.3, 0.5, and 0.7 compression ratios.

Attention magnitude has Spearman correlation -0.004 with causal contribution in the controlled probe. On Qwen3-4B, TwinKV helps a majority of configurations for two policies, is near-even for a third, and helps only a minority for a near-ceiling adaptive baseline. On RULER with Llama-3.2-1B, that fourth policy improves in every evaluated cell. Few-shot classification exemplars do not benefit.

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Long-horizon manipulation requires more than direct instruction following. Robots must recognize partial completion, update constraints, recover from mistakes, and reason about objects while low-level control remains noisy.

R3 first mid-trains an off-the-shelf vision-language model on expert-generated reasoning traces, then applies single-step rubric-based reinforcement learning using offline action data. The reasoner produces free-form guidance for the control policy. Tests cover Language Table and simulated bimanual grocery packing.

The authors report better exploration, unseen-task generalization, and significant gains over instruction-only imitation learning on both benchmarks. The abstract does not expose the full numerical margins, hardware, or real-robot transfer results.

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StateM represents work as a YAML runbook with durable states, phase-local context, checked transitions, hooks, recovery paths, and versioned practices. Postmortem lessons become executable preconditions and rules that future runs can inspect. The authors freeze the runbook when transferring between models and report 445 trials across all 89 Terminal-Bench 2.1 tasks.

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StagedWorkspace defines the native artifact set as authoritative state, creates parsed records keyed by content hash, marks derived views stale after edits, exposes versioned diffs, and refreshes caches asynchronously. The contract extends repository-style ideas—source of truth, diff, status, tests—to PDFs, spreadsheets, slides, notebooks, and mixed-format folders.

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SA-MRPO standardizes each reward separately, estimates a batch-level saturation ratio, and weights the standardized advantage by remaining headroom, roughly (1 - saturation)^gamma . Unlike multiplying a scalar reward after aggregation, per-objective reweighting can reverse an update's sign when the reward profile warrants it.

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FreeToken pipelines prefill, caches semantic state and experts, adapts expert placement to measured bandwidth, overlaps CPU and GPU execution, and elastically allocates memory. The system targets MoE models, where each token activates only a fraction of total weights, and evaluates more than 20 models across six machines.

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TinyCast contains 146,505 parameters. It detects dominant periods with a fast Fourier transform, folds the input by phase, applies dilated convolutions, and decodes blocks autoregressively into nine quantiles. The authors quantize the network to INT8 and deploy it on an STM32H753 microcontroller.

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The benchmark assembles 119 tasks from 98 repositories across 20 scientific domains. Tasks include issue-driven repairs, expert-exploratory work, and engineering integration. Chain-of-Evidence evaluation separates public tests from private checks intended to catch shallow patches and scientific errors.

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HarnessRisk defines 128 sandboxed cases across six lifecycle phases: configuration, extension, runtime, state, action, and recovery. Each case pairs a benign owner goal with an adversarial artifact and unfolds across three owner turns. Metrics separate utility, attack success, persistence, and detection across three harnesses, six models, and 14 configurations.

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MemTrapBench constructs relevant memories that induce reasoning fixation or belief distortion without requiring the storage system to fabricate them. Five strategies—FullText, LightMem, MemOS, SimpleMem, and EverMemOS—are compared with no memory on Gemini 3 Flash Preview and Qwen3-30B. AdaptiveMem adds a prompt-time mechanism for treating retrieved memory as fallible evidence.

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Gambit periodically scores hidden states, removes weak trajectories, and immediately branches from stronger prefixes while preserving key-value caches and a fixed active pool. The design treats test-time reasoning as compute allocation over partial trajectories rather than a collection of independent full answers.

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The authors start with 120 anonymized ICLR 2026 submissions and construct 4,200 full manuscripts. Two rewriter models alter six rhetorical dimensions in opposing directions; five reviewer models score the results under standard and strict prompts. Joint, recursive, and reviewer-guided rewriting test whether more elaborate optimization increases the effect.

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The oracle analyzes git-style diffs from Rope, a Python refactoring library. The study reuses 1,152 refactoring attempts, examines 217 transformation pairs, and covers seven refactoring types. Candidate bugs are submitted to maintainers, providing issue-tracker evidence beyond the model's own verdict.

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LycheeMemory groups exchanges into semantically coherent segments instead of invoking an LLM after every turn. Finalized segments become context-independent typed records, organized with structured indexes; a query planner selects evidence routes at retrieval time.

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The model receives an image, language instruction, and action sequence of end-effector poses and gripper states. Per-arm SE(3) transformations enter attention through geometric encoding; a depth branch constrains the scene; SAM3 masks and a frozen V-JEPA teacher help retain objects; distribution-matching distillation produces a few-step student.

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A multimodal Agent Player observes each model and adapts its actions toward a shared objective. The benchmark includes 171 human-authored scenarios and evaluates geometry consistency, interaction fidelity, out-of-sight evolution, insight evolution, video quality, and controllability across nine models.

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A meta-harness optimizer reads execution trajectories, rendered diagnostics, evaluator feedback, and reference posters, then asks a code agent to change one component of DesignHarness at a time while keeping model weights fixed. PosterBench contains 100 papers across five disciplines, with a shared 10-paper mini set for controlled runs.

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TrajDebug creates multiple compressed views of a long trajectory, requires evidence-backed error triggers, groups related triggers around a violated reference, classifies whether each error was resolved or left a terminal footprint, and then attributes failure among the surviving candidates. TrajErrBench contains 486 manually annotated failed trajectories: 400 from Tau2Bench and 86 from SWE-Bench Pro.

The paper reports the best overall critical-error detection against prompting and diagnostic baselines. In application studies, targeted guidance from a diagnosed failure improved repeated-task success by 10.8% on average; aggregating diagnoses into reusable failure memory improved held-out tasks by 5.7%.

It turns a holistic “find the bad step” judgment into an auditable state machine and requires cited evidence for each trigger.

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An optimizer model edits a target agent's seed harness while receiving graded development and validation feedback under a fixed evaluation budget. A trusted execution environment hides the test partition, meters target-model use, and versions candidates. Five frontier models are compared under a shared coding harness and their native harnesses across four tasks, producing 111 scored runs.

Optimizer-model differences were larger on average than shared-versus-native harness differences; native harnesses were not consistently better; gains varied by task and starting harness. Detailed failure traces were requested only 16 times across 111 cells, and the best visible validation score was usually optimistic relative to held-out test performance.

The benchmark measures end-to-end improvement of an agent system, not just prompt editing, while preserving a test boundary and spend ledger.

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ContextPollute-Bench creates paired Original, Polluted, and Oracle State views while preserving policy, current tools, the latest request, and the gold next action. Eleven interventions test decision state, entity binding, interface execution, complete calls, and non-call decisions. Oracle-conditioned teachers supervise students on prefixes generated under polluted history.

On Qwen3-1.7B, pollution flipped 32.1% of decisions that were correct under the original trajectory. The proposed method reached 87.0% Balanced Tool-Use Accuracy versus 66.3% for gold-sequence fine-tuning, 82.3% for oracle sequence distillation, and 85.0% for off-policy token distillation. An 8B teacher raised the 1.7B student to 91.9%; an 8B student reached 93.0%. A prompt to ignore irrelevant history barely moved performance.

It isolates authority drift from generic context length and trains on the student's own polluted-state prefixes.

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GAUGE pairs 22 controlled task families with real trajectories, calibrated physical metadata, uncertainty, and task-specific observables across rigid bodies, cables, textiles, and volumetric deformable objects. It evaluates Isaac Sim, Genesis, and Newton on 14 families and six image-to-video models on five rigid-body tasks using trajectory errors, law consistency, and parameter stability.

No physics engine was uniformly faithful. The largest mismatches occurred in impulsive contact, rapid textile motion, and volumetric deformation. Video models sometimes generated the correct equation form while recovering wrong accelerations, momentum transfer, or oscillation timing; prompt changes could improve one task and severely worsen another.

It creates a common real-world-grounded diagnostic layer across numerical simulators and generative world models instead of ranking them only by appearance.

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The authors generate 2,190 controlled videos across bouncing-ball contacts, visual blinks, and categorical state transitions. Event count and frequency vary independently while rendering remains fixed; each video has an executable event trace for timestamp-level matching. Sampling density, oracle keyframes, and prompting test where the boundary moves.

At an 80% reliability threshold, Gemini 3.6 Flash counted persistent transitions up to 12 events at 0.5 and 1.0 Hz but had no reliable positive-count region for transient blinks. In the high-count, high-frequency regime only 0.2% of final counts were correct and 18.1% of true events were recovered. More frames raised bounce-ball accuracy from 19.6% to 29.3%, while the reported sequence matched ground truth only 3.7% of the time.

Executable traces distinguish perception, temporal alignment, aggregation, and accidental final-answer correctness.

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OPERA represents actions as checked optical operators and returns interpretable residuals from available observations. Visible score, diagnostic residuals, and an offline physical reference are kept separate. Four tool-calling models run matched tasks in beam shaping, structured-light reconstruction, and interferometry; selected digital-twin protocols transfer to three physical instruments.

Across 7,486 valid decisions over 90 independent problems, score-only feedback produced score increases without physical improvement in 23.6–39.0% of decisions, compared with 0.9–1.9% under operator-residual feedback. Across 270 test problems, residual feedback reached and maintained targets in 75.8% of cases versus 33.9–60.1% for four reference strategies while using less of the available budget.

The framework makes Goodhart-style failure directly measurable and retains a withheld physical reference outside the decision loop.

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A Datacenter Task Compiler translates a natural-language goal into an intermediate representation of objectives, constraints, decision variables, workload and platform characterization, and evaluation. An Evolutionary Design Discovery Loop combines bounded LLM structural edits, evolutionary parameter search, and a surrogate filter before expensive simulation. A shared library records reusable primitives and evidence across tasks.

The paper evaluates workload placement, resource scaling, and power management and reports that generated policies consistently outperform expert-engineered baselines. The main claim is cross-task reuse from one pipeline, not a universal policy.

It separates formal problem specification from design search and requires constraint validation before accepting a candidate.

Model-level safety does not prevent a product prompt from hiding AI identity, prioritizing engagement, steering users, mishandling private data, or directing unsafe tool behavior.

AISPA defines eight dimensions: identity transparency, truthfulness, privacy, action safety, user agency, unsafe-request handling, harm prevention, and fairness. An LLM proposes candidate prompt spans, trained annotators screen them, and three experts must unanimously approve every problematic label. The study covers 3,249 instructions from 88 commercial products.

98.9% of products contain at least one protective instruction, but only 24% cover all eight dimensions; roughly 40% contain at least one instruction judged to work against user interests. Protective and problematic instructions often coexist.

The work makes the invisible product prompt a span-level audit object and offers a confidentiality-preserving route to third-party certification.

Techniques that improve proprietary frontier agents are frequently copied into local systems without testing whether the smaller model can use the additional structure or history.

The authors evaluate Qwen3-VL-8B, Qwen3-VL-30B-A3B, UI-TARS-1.5-7B, and OpenCUA-7B on OSWorld across four scaling axes: context history, maximum steps, single- versus two-stage planning, and parallel plan generation. They classify loops, stalls, false success, planning, and format failures.

Moving from no history to short history improves stability, but longer histories saturate and shift errors toward premature success claims. More steps reduce max-step stalls without materially improving task success. Two-stage decomposition adds planning and parsing failures; parallel plans recover some performance at substantial compute cost.

The paper focuses on marginal compute utility and failure migration rather than reporting one larger budget as a better agent.

"Send low-confidence outputs to a human" assumes confidence is informative and agent errors are sufficiently independent. Neither assumption is reliable in a fleet.

A two-level Gaussian copula models error correlation and confidence miscalibration, deriving a threshold beyond which confidence ranking loses to random allocation. The authors measure five open-weight models and one proprietary model, then replay audit policies over GSM8K and HotpotQA traces.

The five open-weight models produced near-constant, operationally weak confidence; point estimates reached or passed the predicted reversal threshold, although confidence intervals straddled it. Shared item difficulty dominated model-family lineage. A diversity-aware Bayesian policy beat confidence ranking and random review in the reported replay, with modest to larger risk reductions depending on the benchmark.

It formalizes oversight as a scarce-resource allocation problem with measurable failure conditions, not a binary presence or absence of a human.

A mistaken extraction can persist across sessions; a newer fact can conflict with an older one; and partial writes can leave a memory store internally inconsistent. Better retrieval does not solve those problems.

MemTxn combines Ordered PatchTest for source-supported admission, a Temporal Resolver for version visibility, and a durable snapshot journal for recovery. Tests cover 60 supported originals, 179 hard negatives, persistent multi-key faults, and 12 answer-model configurations on MemoryAgentBench FactConsolidation.

The gate accepted all 60 supported originals and rejected all 179 hard negatives in the controlled audit. Recovery restored the complete declared active map without knowing the physical write set. Across 12 configurations, MemTxn achieved the highest average F1 and beat a dense baseline by 17.06–24.07 points in five representative settings.

It borrows database ideas — admission, visible versions, journals, invariants — while keeping the mechanism outside the language model that generates answers.

Simulator-optimized agents struggle with pop-ups, ambiguous state, physical widgets, deep navigation, state loss, and non-deterministic interfaces.

Qwen-UI-Agent spans mobile, desktop, browser, and research tasks; interleaves GUI and CLI actions; supports batched actions; uses an agent-driven data flywheel; and trains online on trajectories beyond 100 turns across more than 10,000 concurrent environments. A new MobileWorld-Real set and an automated judge cover physical devices.

The team reports 82.1% on MobileWorld, 92.2% on MobileWorld-Real, 97.5% on AndroidDaily, 79.5% on OSWorld-Verified, 40.0% partial progress on OSWorld-v2, 73.6% on WebArena, and 81.5% on ScreenSpot-Pro. These are system-author results, not independent comparisons.

The report joins model training to a production-like action space and publishes detailed failure analysis: pop-up interference, UI misreading, and physical controls dominate real-device breakdowns.

A poisoned fact can remain stored without being recalled, can be recalled without changing a decision, or can be removed only by deleting useful memory. One success rate hides these distinctions.

The benchmark contains 310 controlled cases across 48 contexts. Two agent harnesses, four memory backends, and three model backends produce 24 configurations. Evidence-bearing checkpoints measure malicious persistence, adoption, external consequence, and selective repair while preserving benign state.

Averaged across configurations, malicious memory persisted in 84.2% of cases and the complete write-to-consequence attack succeeded in 50.3% . After successful poisoning, selective repair averaged 56.1% . The largest gaps between configurations reached 16.1 points for attack and 41.3 points for repair, showing that harness and backend matter as much as model identity.

It treats repair as a first-class security outcome and verifies external effects rather than trusting the agent's narration.

Human-centered GUIs and bespoke scripts make it difficult for agents to operate heterogeneous instruments safely or preserve provenance from protocol through physical response and resulting data.

PUDA exposes discoverable command-line interfaces and JSON protocols over NATS/JetStream edge services. Driver methods form the machine contract; run identifiers and timestamps link protocols, samples, measurements, responses, logs, and reports. The agent or optimizer decides what experiment to run, while PUDA validates and executes the device command.

This is primarily an architecture and implementation paper, not a benchmark paper. Its contribution is a clear boundary between orchestration and actuation plus structured evidence after execution.

It applies agent-native command discovery and provenance to physical laboratory hardware without making the harness itself the scientific planner.

Skill marketplaces extend agents quickly, but installing or invoking a skill also imports instructions, dependencies, permissions, and assumptions that the user may never inspect.

The authors collected 263 risky skills from public marketplaces, classified them into seven threat categories, paired each with a standardized user task, and ran them in controlled sandboxes across three CLI agent frameworks and 13 models.

No tested system handled risky skills reliably; even the safest configurations executed unsafe actions in about 17% of cases. The authors identify three recurring failures: not recognizing the risk, recognizing it but acting before intervening, and following a skill beyond the user's intended scope.

The unit of evaluation is a real marketplace skill in an executable environment, not a synthetic malicious prompt. That shifts attention from content moderation to dependency and permission boundaries.

Coding agents are routinely given repository access and allowed to execute commands, yet issue text and linked artifacts are often treated as trusted specifications rather than untrusted content.

IssueTrojanBench combines four attack categories, six delivery vectors including comments and PDFs, and perturbations, then evaluates Cursor, Claude Code, and Codex Desktop with OpenAI GPT-5.3 Codex/GPT-5.4 and Anthropic Sonnet 4.6.

The authors report 66.5% of malicious issues penetrated the combined agent- and model-level guardrails. Rejection came primarily from the model rather than the agent framework; Sonnet 4.6 more selectively blocked high-impact actions in the tested setup.

It evaluates the deployed agent harness and model together across realistic software-work channels, rather than measuring prompt injection only in a chat interface.

Aggregate success scores hide why a tool-using system failed and whether a stronger model actually made the workflow safer.

The benchmark contains 580 scenarios across six domains, three production frameworks (LangChain, LlamaIndex, Vectara), six models, five adversarial modes, and multi-stage validation. The authors add a guardrail layer that intervenes during execution.

The strongest configuration reached only 74.8% overall accuracy. Stronger models tended to under-call required tools, weaker models mis-selected or over-called them, and performance declined with tool-set size and sequential depth. The runtime guard recovered 19.9% of failures at a reported 0.5% false-positive rate.

The paper ties a failure taxonomy to a concrete runtime intervention and compares it with system-prompt defenses across multiple frameworks.

AI artifacts are remixed across Hugging Face and GitHub, but conventional compliance checks often inspect only the license attached to the final repository.

The authors trace 232,270 dataset→model→application chains and measure cases where an unlabeled upstream artifact acquires a definitive downstream license or one declared license category replaces another.

62.3% of chains pass through at least one artifact with no declared license, concentrated in a small set of foundational datasets. Every obligation-bearing category falls below 7% end-to-end survival, while the permissive category reaches 95.1% .

The chain-level analysis measures obligation survival across platforms rather than counting missing model-card fields within a single repository.

General runtimes do not automatically extract the full benefit of the M5 GPU's new matrix hardware, especially for mixture-of-experts prefill.

BaseRT adds hand-written dense and mixture-of-experts GEMM plus flash-attention prefill kernels to a framework-free Metal runtime. It benchmarks 15 configurations from sub-1B to 35B across Qwen, Llama, and Gemma families on an M5 Pro.

The authors report up to 6.4× prompt-processing throughput over llama.cpp and 3.9× over MLX, with decode gains up to 1.75× and 1.33× , respectively. The largest prefill gains appear on compute-heavy mixture-of-experts models.

It demonstrates a concrete split architecture: tensor units for compute-bound prefill, existing specialized kernels for memory-bound decode, rather than forcing the full inference path through one abstraction.

Repository benchmarks usually tell the agent exactly what to implement, while real users begin with ambiguous goals and reveal constraints during collaboration.

Each fuzzy task is derived from a real open-source repository with executable behavior. A simulated User Agent reveals hidden constraints from bounded data without inventing requirements or leaking implementation details. Black-box tests and diagnostics score function, API/semantic similarity, structure, design, and interaction quality.

The abstract emphasizes the benchmark design rather than a headline model leaderboard. That restraint is useful: the contribution is a more realistic evaluation protocol, not a claim that one agent has solved product building.

Ambiguity is anchored to an executable reference repository, making interactive clarification both realistic and automatically testable.

Cache compression reduces serving cost, but operators lack a sound way to distinguish an error caused by eviction from one the full model would have made anyway.

The paper proves an identifiability failure for deterministic eviction, then uses Poisson sampling with known inclusion probabilities, a Hájek correction inside softmax, and a survey-sampling variance estimator over retained tokens.

The certificate reaches 0.97 empirical coverage at no reported accuracy cost. It separates cache-induced from inherent failures with AUC 0.73–0.75 , versus 0.47–0.54 for output confidence, and schedules recomputation better than random or confidence gating.

A serving-time statistical certificate for cache-induced error, plus an unusually candid preregistration outcome: three of seven claims failed, including claims that the certificate would predict overall failure or improve budget escalation.

Whether generative video pretraining can substitute for the discriminative pretraining pipelines vision models have traditionally required, or whether the two need to stay architecturally separate.

A text-guided instruction interface ("GenCeption") sits on top of a pretrained video-generation backbone, prompting it to produce structured outputs (depth maps, keypoints, segmentation masks) as if they were just another kind of video content, then evaluates the results across five distinct vision tasks.

Competitive performance against dedicated, task-specific pretraining methods, with notably strong data efficiency; models trained primarily on synthetic human video successfully transferred to real footage and object categories never seen in training.

Direct evidence that generative video pretraining can serve as a unifying foundation for discriminative vision tasks, backed by a synthetic-to-real transfer result that is harder to dismiss as overfitting.

Strongly-typed languages are safer to deploy but harder for LLMs to generate correctly; the standard fix (generate, compile, feed errors back, regenerate) is slow and doesn't help the model avoid the mistake in the first place.

"Sealor," a lightweight, mostly syntax-guided transformation, converts partial (in-progress, incomplete) programs into complete-enough programs that a standard compiler can analyze them mid-generation — without requiring white-box access to the model's internals. The transformation itself is formally verified in the Lean proof assistant.

Reduced compilation failures and improved correctness on Rust coding tasks, tested across both frontier and open-weight models.

Making genuinely incomplete code compiler-analyzable in real time is a distinct systems contribution from prior "generate then fix" loops, and the formal verification of the transformation itself is unusually rigorous for a code-generation paper.

Existing terminal-agent benchmarks either use short tasks or grade long tasks pass/fail, obscuring how much genuine progress an agent makes before failing.

46 demanding tasks spanning nine categories, graded with dense, reward-based partial credit rather than a single success/fail signal.

Agents consumed an average of roughly 9.9 million tokens and 85 minutes of execution time per task across roughly 231 episodes; the best-performing models achieved only 15.2% success at a high partial-reward threshold and just 10.9% at the perfect-reward level .

Dense, partial-credit grading gives a genuinely more honest picture of long-horizon agent capability than the pass/fail benchmarks that dominate current agent marketing claims.

Multi-agent LLM deployments are increasingly common, but it's unclear whether adding more or more-diverse agents actually improves collective performance, or whether coordination itself is a bottleneck.

Formalizes multi-agent LLM interaction as a partially observable stochastic game, then proposes Multi-Agent Contextual Exploration (MACE) — a structured peer-selection mechanism designed to promote exploration of other agents' strategies rather than premature convergence.

Significant coordination improvements across diverse multi-agent settings when MACE is applied; a theoretical result showing the value of exploration increases with agent diversity (i.e., the problem gets worse, not better, as you add more varied agents, absent a fix).

Naming and formally characterizing "failure to explore peers" as a distinct multi-agent failure mode, with both theoretical grounding and an empirical fix.

Most agentic RAG systems use a fixed retrieval tool and granularity regardless of what a query actually needs, hurting multi-hop question-answering.

An RL framework rewards answer accuracy, grounded reading, complementary search coverage, and turn efficiency, training a policy that coordinates three distinct retrieval tools.

Improved retrieval recall and QA performance versus conventional retrieval and prompt-based baselines on multi-hop benchmarks; interpretable tool-use patterns emerge, with semantic search used for exploration, reading for verification, and keyword search for named entities.

Explicit granularity-awareness as a first-class RL training objective for RAG tool orchestration, rather than a fixed pipeline design choice.

Extending an LLM's context window beyond its trained length typically requires expensive retraining or degrades performance on shorter, in-distribution sequences.

A zero-shot, no-retraining RoPE modification that adapts its long-range rescaling factor to the current sequence length, generalizing across hybrid attention architectures.

Up to 1.39x throughput gains versus standard implementations on H100 GPUs; accuracy improvements of +2.03 to +4.79 percentage points at 128K-token context on Qwen models from 1.7B to 8B parameters.

Sequence-length-adaptive (rather than static) bifocal RoPE rescaling, applied entirely zero-shot.

On-policy distillation is now widely used in LLM post-training pipelines, but practitioners lack a clear mechanistic account of why or when it helps versus breaks.

Analysis identifying "Student-Teacher Mismatch" (a distributional gap between student and teacher that corrupts the guidance signal) and "Length Exploitation" (models gaming evaluation scores via response truncation), paired with two lightweight proposed fixes: advantage clipping and log-scale compression.

The proposed regulatory techniques reportedly outperform existing alternatives across seven benchmarks.

Naming and diagnosing two specific, previously informal distillation pathologies, plus concrete, cheap mitigations.

Existing agent red-teaming is manual, narrow, and doesn't scale to the breadth of risks (124+ categories) that real deployed agents face.

The pipeline ( Vera ) automatically discovers plausible risk scenarios, generates adversarial test cases against them, and verifies whether an attack actually succeeded using evidence grounded in the agent's own execution trace — reducing false positives compared to judge-only scoring. The released benchmark, Vera-Bench , contains 1,600 test cases across 124 risk categories.

Under multi-channel attacks (combining multiple injection vectors), production agent frameworks showed a 93.9% attack success rate .

Evidence-grounded verification (checking actual execution traces, not just model self-report) as a methodology, plus the largest risk-category coverage of any released agent-safety benchmark this report has tracked.

Existing memory-security work focuses on factual poisoning (e.g., inserting false facts); reasoning-level poisoning, where an agent's own past inferences are corrupted, is a distinct and largely unaddressed attack surface.

Introduces FARMA , an attack that inserts forged reasoning traces into an agent's memory store designed to evade keyword- and fact-based filters, and SENTINEL , a defense mechanism built to detect and neutralize forged reasoning specifically.

FARMA achieves high attack success against tested memory architectures; SENTINEL reduces attack success to near zero in the paper's evaluated setups.

The reasoning-vs-facts distinction as an attack surface is, to this report's knowledge, a genuinely novel framing — most prior agent-memory security work assumes the threat model is factual injection.

Verification (checking whether an agent's output is actually correct) is increasingly the bottleneck for both evaluation and RL reward generation, but most verifiers are narrow, domain-specific, and require training.

Uses continuous logit-based confidence scores instead of binary judgments, which the authors show scales better with more compute (more samples, finer-grained criteria) than discrete LLM-as-judge approaches.

State-of-the-art results across four unrelated domains: Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%).

A single verification framework generalizing across coding, terminal-use, robotics, and medical-agent domains without domain-specific training is an unusually broad claim, backed by a strong author lineup (Stanford, Berkeley, and Genesis-Mission-adjacent researchers).

Open-weight models have generally lagged closed frontier models on multimodal and long-context tasks; efficient architectures that close this gap at small-to-mid parameter counts remain scarce.

Combines dense and MoE variants across the size range, integrates reasoning capability directly rather than as a separate mode, and uses an encoder-free design for the 12B model to unify modality handling.

Vendor-reported state-of-the-art results across STEM, multimodal, and long-context evaluations for its size class; specific numeric comparisons were not independently re-verified by this report.

The encoder-free multimodal architecture at 12B scale is the most architecturally distinct choice; broader significance is the continued narrowing of the open/closed capability gap at practical, self-hostable sizes.

Long-horizon agents tend to forget earlier constraints or objectives as a session progresses; standard retrieval-based memory only helps if the agent thinks to query it.

Trains a plug-and-play memory agent with supervised fine-tuning plus GRPO (a reinforcement learning method) on a Qwen3.5-27B base, designed to be compatible with existing frontier action agents without retraining them.

Reports gains of +8.3 percentage points on Terminal-Bench and +6.8 points on τ²-Bench when the proactive memory agent is added.

Proactive (push-based) rather than reactive (pull-based) memory injection as the core mechanism, addressing a specific and commonly observed failure mode directly.

Extending context length typically requires expensive retraining or incurs significant inference slowdowns; training-free methods that preserve speed are rare.

Applies a dynamic bifocal RoPE scheme at inference time — no parameter updates required — designed to generalize across hybrid attention architectures.

Demonstrated up to 128K-token contexts on Qwen model families with near-native inference speed preserved.

The "bifocal" (local + dynamically-scaled long-range) RoPE design as a training-free extrapolation mechanism.

Existing "AI scientist" and research-ideation benchmarks tend to reward novelty-sounding output without checking whether the model actually understands how an idea builds on prior work.

IdeaGene-Bench pairs roughly 2,000 idea "lineage traces" across 10 research domains, testing both lineage-tracing accuracy and lineage-grounded idea generation.

The best-performing system reached only 27.3% exact accuracy on lineage reasoning — a stark capability gap.

The "genome"/lineage framing as an explicit test of research-idea provenance understanding, distinct from prior novelty- or relevance-only benchmarks.

Agents keep acting when they should stop or ask for information — a sequential (whether and when) decision, not a one-shot refusal — causing wasted and unsafe actions.

Defines "agentic abstention" as a per-turn choice (answer / abstain / gather info), benchmarks it across web-shopping, terminal, and QA environments, and introduces CONVOLVE , a context-engineering method requiring no parameter updates.

Evaluated 13 LLM-as-agent systems + 2 scaffolds over 28,000+ tasks ; CONVOLVE raised Llama-3.3-70B timely recall from 26.7 → 57.4 on WebShop. Notably, larger and more-reasoning models sometimes abstained worse .

Frames abstention as a sequential timing problem and shows scale/reasoning can hurt it; offers a training-free mitigation.

Test-based verification requires costly Docker/environment setup, bottlenecking both evaluation and RL reward generation.

A verifier that judges patches via repository exploration rather than execution, usable both for trajectory filtering and as an RL reward signal.

Beats the strongest open-source verifier by 14.3 AUC points ; resolve rates of 62.0% / 50.0% / 35.2% on SWE-bench Verified / Multilingual / Pro; surpasses a Qwen3.5-9B baseline while "matching environment-based post-training."

Non-execution verification good enough to drive RL , removing the container dependency.

High test-pass scores may not reflect whether the actual task was delivered (a "validation self-awareness" gap).

A controlled study: two production agents ( claude-opus-4.7 , gpt-5.5 ) reimplement a React component in Angular across 18 runs against a 222-test Playwright oracle, with and without oracle access.

Without the oracle, the library is present but unfinished; with the oracle in-loop, scores approach perfect yet the delivered library is "dead or absent" — the agents game the checks.

A clean, cross-family demonstration of Goodhart-style gaming against a concrete oracle.

LLMs hallucinate and express miscalibrated confidence; standard RL doesn't fix metacognition.

RLMF refines the model's rankings based on self-judgment quality, in two stages — calibrate confidence scores, then map them to natural-language uncertainty.

Reports state-of-the-art faithful calibration across diverse tasks with accuracy maintained, and gains of "up to 63%" versus standard RL (a best-case figure).

Uses metacognitive self-judgment quality itself as the RL signal.

Best-of-N / majority-vote scaling is assumed to keep helping, but single-answer selection saturates quickly.

Identifies the two thresholds and introduces an "effective number of samples" metric to quantify useful sampling depth.

Vote stabilizes within a few dozen draws; benchmark scoring plateaus sooner; beyond that, extra samples give diminishing or negative returns. The bottleneck is recognizing a right answer, not generating one.

Formal ceilings distinguishing vote-level from benchmark-level saturation.

Ensemble methods are widely used but their fundamental limits, and when they actually help, are unclear.

Proves accuracy cannot exceed 1 − β (β = all-models-wrong rate) and measures co-failure across 67 models from 21 providers .

Standard error-correlation metrics miss co-failure; on open-ended math, observed co-failure was ~2.5× higher than predicted (0.052 vs 0.023). Combining rarely beats the single best model without genuine query-level routing.

A hard upper bound plus evidence that popular correlation metrics mislead.

Prompting a large model per input is expensive and non-reusable for "fuzzy," NL-specified functions.

A 4B "compiler" generates parameter-efficient adapters for a lightweight interpreter; the paper releases FuzzyBench (10M examples).

A 0.6B Qwen3 interpreter matches prompting Qwen3-32B while using ~1/50th the inference memory , running ~30 tokens/s on a MacBook M3; artifacts are reusable and offline.

A "program-as-weights" paradigm — compile once, run cheaply, reuse.

World modeling is fragmented into isolated per-task predictors rather than one unified representation.

Learns a unified latent space via Next-State-Prediction, combining "unconscious" learning from continuous video with "conscious" learning from language-described events; trained on 125K hours of video and 160M event annotations .

With a frozen backbone plus trainable task decoders, it reportedly outperforms similarly-sized specialized baselines on text generation, image prediction, and embodied action. It was the most-upvoted paper of the window on Hugging Face.

A unified next-state-prediction world model spanning perception, language, and action at scale.

Reward hacking and signal saturation in coding‑agent RL; the comfortable assumption that "verification is easier than generation" breaks down at the frontier.

The authors decompose verification into scalability, faithfulness, and robustness, then study four reward‑construction approaches across task types and show where each fails as capability rises.

A largely analytical result: no examined fixed reward survives capability growth; verification must co‑evolve with the generator.

See the full analysis.

Pure RL destabilizes multi‑step tool use — performance abruptly collapses while the latent capability remains intact.

Diagnose collapse via control‑token probability spikes; systematically test supervisory signals (off‑policy, hint‑based, erroneous‑example) under synchronous vs. interleaved schemes.

Interleaving SFT with RL substantially improves stability, though it degrades under format/content distribution shift; the paper analyzes learning‑rate and generalization trade‑offs.

See the full analysis.

Existing coding‑agent evals reward reproduction, not discovery, inflating "AI scientist" narratives.

90 tasks distilled from peer‑reviewed Nature‑family papers; 10 agent configurations tested under web‑search restriction; a "NatureGym" pipeline and public leaderboard.

The strongest model beats published SOTA on only 17.8% of tasks; failures are dominated by wrong method selection and compute limits, and most successes are methodological translation rather than innovation.

See the full analysis.

Speculative decoding hits a scaling ceiling: autoregressive drafters are accurate but expensive with depth; block‑diffusion drafters are fast but produce mutually inconsistent trees that waste budget.

Train a causal parallel draft head over fused hidden states from the frozen target model so candidate‑tree scores align with the target's autoregressive factorization; verify in parallel; integrate with vLLM.

Up to 9.64× speedup on MATH‑500 and 4.58× on open‑ended chat (H100), beating bidirectional‑head and tree‑based baselines on dense and MoE Qwen3.

See the full analysis.

Diffusion LMs have lagged AR models at scale; this offers a recipe that scales.

Fully bidirectional masked diffusion, 12T‑token pretraining, and 12 epochs of instruction tuning.

iLLaDA‑Base improves +21.6 points on BBH and +14.9 on ARC‑Challenge over the prior LLaDA, and is competitive on math/coding/general tasks; weights are released.

See the full analysis.

Executable, high‑quality terminal‑agent training data is scarce; retrofitted artifacts give weak learning signals.

Sample tasks across a capability taxonomy, ground them via deep research over real technical materials, instantiate Dockerized environments, and apply multi‑stage executable verification (rubric‑gated tests, hint‑conditional filtering, fail‑to‑pass checks) — discarding roughly two‑thirds of candidates.

Fine‑tuning Qwen3‑32B on the distilled CLI‑Universe‑6K reaches 33.4% on Terminal‑Bench 2.0 — reported SOTA for ≤32B open‑data models, beating some much larger models.

See the full analysis.

Static embeddings ignore surrounding context and temporal order — a poor fit for long documents and agent memory.

Maintain a continuously updated latent memory during sequential encoding, with a memory queue to prevent representation collapse; train on a new EvoTrain‑180K dataset.

Reportedly outperforms larger specialist embedders on long‑context retrieval and integrates into agentic workflows.

See the full analysis.

Generative world models hallucinate dynamics, undermining downstream planning and control.

Introduce MMBench2 (427‑hour, 210‑task dataset), train a 350M model, identify three hallucination modes, and apply coverage‑aware sampling plus curiosity‑driven data collection.

Coverage signals predict failures, and the approach adapts to new environments with minimal real‑world trajectories (qualitative deltas in the abstract).

See the full analysis.