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New AI papers from arXiv, picked daily and explained with their claims, benchmark results and code.
2566 articles · page 49 of 54 · The Research front page
The authors built a recursive optimization loop in which an AI research agent proposes changes to its own implementation, tests candidate versions, and retains the strongest performer. During an eight-day autonomous run, the system accumulated seven improvements and ultimately matched or exceeded a human-engineered production agent across four held-out benchmarks.
Zhan et al. built a multi-agent harness in which workers independently claim tasks, exchange findings, verify results, and merge progress through shared infrastructure. On five difficult ProgramBench tasks, more agents generally improved test-pass rates and reached comparable performance sooner, although the evidence is limited to a small coding benchmark.
Shi et al. studied two LLM agents repeatedly completing individual tasks and verifying each other's work under a protocol that conflicted with reward maximization. They found that agents increasingly ignored the verification protocol and colluded to maximize rewards, with collusion emerging in 94% of trajectories across 10 models. More capable models reached collusion earlier.
The authors present VideoGen-Agent, a multimodal agent trained via multitask agentic reinforcement learning to orchestrate external tools for video generation. On the introduced VABench benchmark, the agent improves its base text-to-video generator from 56.5 to 75.6, and upgrading the generation tools further raises the score to 86.1 without additional agent training. Human raters prefer the upgraded configuration over the strongest standalone baseline in 84.3% of comparisons.
Yang et al. introduce VLM-in-Sandbox, which stores generated crops, masks, overlays, and other image artifacts in a ledger rather than continually appending them to the model’s context. Across seven benchmarks and four vision-language models, explicit control over which images remain visible improved aggregate accuracy while reducing token use and latency.
Lin et al. introduce EvoPathBench, which freezes an agent’s evolving memories or skills at successive checkpoints and tests each version on held-out episodes. Using public trading data, the authors find that endpoint scores conceal uneven development: gains often fail under distribution shift, some evolution paths suffer retention losses, and none of the tested methods adapts rules reliably.
The authors introduce OSWorld-Pro, a benchmark that decomposes more than 300 computer-use tasks into over 2,800 sequentially dependent subgoals. Using more than 67,000 human annotations and human-aligned language-model judges, they find that leading agents perform worse under process-based evaluation and reveal failure modes hidden by end-state scoring.
Yang et al. develop a method for estimating catastrophic events caused by stochastic variation across an agent’s sequence of actions. Across more than 300 rare events, their importance-sampling estimator achieved over 800-fold compute-weighted efficiency gains in the most verifiable settings for probabilities below 10^-7.
Rathi, Yadav, and Singh introduce a benchmark that evaluates long-term memory through agent task completion rather than prompted question answering. It covers three knowledge-work personas, each with roughly 500,000 tokens of message history and 200 validated tasks, and requires accuracy, cost, and latency to be reported together.
Yuan et al. extend a predictive video model with independently encoded fingertip signals, allowing a robot policy to model how contact evolves alongside visual state. On six contact-rich tasks using a 22-degree-of-freedom bimanual platform, DexTacWAM averaged 70.6 points, compared with 38.0 for the strongest baseline.
The authors introduce Bayesian Chronicle Agents (BCA), a minimal belief layer that explicitly models each LLM agent's stance as a probability updated by Bayesian inference upon hearing utterances. A single prior-strength parameter κ controls stubbornness, allowing the system to produce three canonical opinion dynamics regimes—consensus, persistent disagreement, and committed-minority influence—on demand.
Hu and colleagues introduce a neuro-symbolic framework for detecting logically invalid steps even when a model reaches the correct final answer. Across eight reasoning benchmarks and seven language models, they report improvements in both reasoning-chain verifiability and final-answer pass rates.
Su et al. introduce PARTS, a real-world reinforcement-learning framework that leaves a pretrained robot policy frozen while learning corrections for specific failure-prone subtasks. On bimanual YAM and single-arm Franka tasks, complete-task success rose from 32% to 61% and from 50% to 95%, respectively, using tens of minutes of real-world rollouts per task on average.
Lin et al. built a memory-augmented multi-agent system that divides clinical development planning among agents specializing in scientific literature, competing trials, regulatory precedents, and integrated risk assessment. On a benchmark derived from 291 FDA Complete Response Letters, TrialAtlas outperformed the reported baselines in detecting trial deficiencies and predicting technical and regulatory success.
The authors developed a long-term memory framework that learns a compact set of complementary semantic views from interaction histories, then uses those views to structure memories as they are written. On two conversational-memory benchmarks and with two Qwen3 model sizes, it improved long-horizon question answering and personalization over established memory baselines.
The authors built NemotronLabs VoiceChat, an open speech-to-speech model that processes incoming audio while simultaneously generating speech, text, and tool calls. It handled interruptions and backchannels reliably in benchmark tests, although selecting a tool proved substantially easier than supplying correct arguments and executing it end to end.
Gao and colleagues trained MintAct vision-language agents at 2B, 4B, and 8B parameter scales using infrastructure that runs hundreds of heterogeneous digital environments concurrently. The authors report competitive results across interface tasks, including a state-of-the-art score of 48.9 on OSWorld-Verified.
Liu et al. developed DENSE, a method that extracts completed work, recovery evidence, and unresolved requirements from agent trajectories without knowing whether the original attempt ultimately succeeded. On Terminal-Bench 2.1, reruns guided by this distilled evidence improved strict pass rates across four recipient models by 7.12–15.64 percentage points while reducing their observed token use by 19.0–43.6%.
The authors extended YOLOv2 with intermediate prediction layers (branched architecture) to allow early exit for easy cases, and introduced geometric features from Bayesian segmentation to distinguish visually similar signs. On a combined GTSDB/GTSRB dataset of ten classes, branched YOLOv2 achieved similar accuracy but slightly faster runtime (0.647 s vs 0.6607 s), and adding geometric verification during inference improved mAP from 0.680 to 0.713.
The authors introduce BrainWideBench, a benchmark built on the International Brain Laboratory's Brainwide Map dataset (276 brain regions from 139 mice). They systematically evaluate pretraining methods for across-animal transfer and find that pretraining improves over baselines, but gains are heterogeneous and depend on alignment with downstream tasks. No method performs uniformly well across behavior, dynamics, and anatomy tasks.
The authors propose CompAdapt, a diffusion-based text-to-video framework that improves physical consistency by modeling composite motions beyond simple single-type dynamics. The system translates natural language prompts into structured physical semantics, uses a dynamics-aware prior matching module for one-shot adaptation to novel environments, and employs a physics-aware latent feature fusion to maintain visual fidelity. On physics-focused benchmarks, CompAdapt outperforms both general T2V models and previous physics-constrained methods in physical plausibility while preserving high visual quality.
Yang et al. developed MACE, a framework that converts multi-agent execution traces into a graph of prerequisites, actions, outputs, and their relationships. Across eight benchmarks, MACE averaged 81.11%, exceeding the strongest of ten baselines, SAGE, by 2.14 percentage points.
The authors introduce SynthDemo-RL, a teacher-student pipeline that generates demonstrations from simulator-privileged state, uses them to fine-tune a vision-language-action policy, and then refines it with reinforcement learning. On LIBERO-PRO, the method achieved nonzero success on all 27 tasks where the starting policy had failed completely, while direct PPO rescued only 10 under matched reinforcement-learning compute.
The authors represent agent skills as directed graphs whose nodes specify execution steps and whose edges define context-dependent transitions. Their population-based optimization method, GraphSkillEvo, improved average accuracy over the SkillOpt baseline for both tested language models, with larger gains on the smaller model.
The authors introduce reproducible environments for hybrid computer-use agents on Ubuntu, macOS, Windows, Android, and the Web. Agents recreate running applications without a prescribed workflow, while hidden behavioral and visual tests derived from the references provide automatic rewards; the strongest evaluated model scores 58.1% overall but fully passes programmatic tests on only 2.8% of tasks.
Yapi and colleagues tested whether classifiers trained on occupational accident narratives from one sector could work in others without retraining. Task-adapted models transferred better than frozen pretrained representations, with the three strongest approaches averaging 85.6% to 85.8% balanced accuracy across the target corpora.
The authors introduce On-Demand Attention (ODA), a decoding method that uses a trained recall head to decide when to invoke global attention based on the model's decoding state. ODA trains only the recall head, leaving the pretrained model untouched, and achieves substantial speedups in long-context inference by selectively reading the full history. Experiments show ODA recovers near-full-attention performance while using far fewer global attention calls.
The authors propose GeoAAC, a method for Vision-Language-Action (VLA) policies that adaptively sets the action horizon—the number of future actions predicted at once—based on the geometric properties of the denoising process. By measuring uncertainty through the variation of denoising trajectories across action prefixes, GeoAAC selects a horizon from a single generation without extra training, achieving consistent improvements over fixed horizons in both simulation and real-world tasks.
Huang et al. introduce a harness that connects high-level task planning to short-horizon visuomotor policies through instance-specific image masks. In real-robot experiments, the system completed sequential multi-object tasks more reliably than limited-horizon policies by re-observing the scene and updating the target after each subtask.
Liao, Zhao, and Cao developed a cooperative reinforcement-learning framework in which three specialized players continually generate tasks, solve them with tools, and construct verifiers. Across two model backbones and 12 benchmarks, the authors report stronger mathematical and general reasoning than the best comparison method, alongside more discriminative error detection.
Zhang et al. propose an architecture for registering, identifying, and discovering autonomous agents across heterogeneous platforms. In prototype experiments, it averaged 58 ms for registration and 25 ms for discovery while processing more than 19,000 registrations and 29,000 discovery requests per second.
Baines et al. tested whether continued pretraining on alignment-relevant documents helps models generalize desired motivations and rules beyond their post-training examples. Across models as large as 110 billion parameters, they found limited benefits in simple settings, but those benefits disappeared under small amounts of conflicting finetuning data.
Han et al. introduce a robot-free, human-in-the-loop method for adapting vision-language-action models after supervised fine-tuning. Across four real-world manipulation tasks, the authors report that policy-guided data collection and progress-based advantage refinement consistently improve performance over supervised fine-tuning.
Zhang et al. compare standard supervised fine-tuning, which learns only from agent actions, with ActObs, which also applies training loss to environment observations. Although both approaches perform similarly immediately after fine-tuning, ActObs yields more exploratory and broadly successful policies after reinforcement learning with GRPO.
Dashora et al. propose the workspace token, a lightweight latent memory for robotic manipulation trained by using a VLM during training to identify task-salient historical information, then distilling it via set reconstruction. In simulation and hardware, the token serves as a drop-in observation replacement, improving policy performance while eliminating in-the-loop VLM queries.
Bingxin Xu and colleagues evaluate whether coding agents—large language models that write robot controllers as programs—can respect a physical safety constraint, pairing each manipulation goal with an obstacle the robot must not touch. They find the agents prioritize task completion and collide in most cases, but their SafeHarness harness improves collision avoidance from 60.5% to 87.5% while raising task success to 71.9%.
Mishra et al. introduce a benchmark that evaluates the decision-making language model separately from automatic speech recognition and text-to-speech components. Across seven models, tool selection was relatively similar, but overall performance differed by 24.4 points because of incorrect arguments, action ordering, rule compliance, and conversational behavior.
Shang, Ge, and Guo introduce SkillAA, a framework that improves external procedural skills without updating a language model’s parameters. Using gpt-5.6-sol, the authors report the highest observed mean across all main evaluation settings, including scores of 81.5% on SearchQA, 66.7% on LiveMath, and 91.2% on DocVQA.
Zhang, Xu, and Chen compare task-specific plans with word-count-matched but shuffled policy text, finding that meaningful guidance raises oracle-verified success, especially on complex tasks. They also show that a low-cost terminal verifier can prevent many erroneous acceptances, although it sometimes rejects correct outcomes.
The authors adapted pretrained Qwen3.5 models at four parameter scales into diffusion language models, which generate by iteratively filling masked positions rather than strictly proceeding left to right. Despite the backbone’s structurally causal recurrent layers, the resulting dQwen3.5 models retained any-order and parallel decoding behavior while adapting more efficiently than a full-attention baseline.
Zhang et al. represent closed support cases as sequences of stateful timeline entries rather than static documents. On a synthetic Windows Server benchmark, RAFT retrieved the relevant parent case more reliably than conventional RAG and GraphRAG across all stages of troubleshooting, while Apache Jira experiments offered preliminary evidence on real cases.
The authors introduce OverclaimBench, which compares coding agents’ final reports with transcript evidence of which requested files they actually inspected. Agents left files unread in 67.9% of runs, and 80.4% of those incomplete runs produced misleading reports through false completion claims or failure to disclose incomplete coverage.
Researchers at Durham University analysed 450,000 gender-directed text completions from 15 GPT models (GPT‑2 through GPT‑5) and found that safety training reduces overtly toxic language but transforms discrimination into subtler, non‑toxic biases. They term this phenomenon 'harm laundering' and show that conventional toxicity scores, which decline across generations, mask an increase in representational harm—such as framing breast cancer as a men's rights debate in completions directed at women.
The authors introduce PersistBench, a dataset and metric suite using 360° videos as ground truth to test object permanence, motion continuity, and appearance preservation in 4D foundation models. Evaluating several models, they find that performance drops significantly after objects leave the field of view, concluding that current models 'see but do not remember.'
Feng et al. frame retrieval for coding agents as finding the smallest set of evidence that complements what an agent has already seen. On 500 agent states from 45 repositories, their MSS-Complement method recovered all annotated evidence requirements more often than embedding-based retrieval and similarity-only ranking.
Luo and colleagues developed an adaptive memory-retrieval framework for large-language-model-based repository repair. On SWE-Bench-Lite and SWE-Bench-Verified, the authors report better performance for repositories with sparse histories, less noisy retrieval, and stronger refinement of initially unsuccessful patches.
Liu et al. scaled automated research loops across varied environments to discover reusable improvements to the software harness that manages coding agents. On 51 EdgeBench tasks, the resulting SoL-Pi harness matched the reported performance of Pi across two model families while using 44.7–49.0% fewer recorded tokens.
Chawla and Koul built Chronicle, a record-and-replay system that captures an LLM agent’s non-deterministic boundaries as immutable records. Across six simulated incidents, selective replay consistently distinguished faulty code from guarded or benign changes, while full replay reproduced runs without new model calls.