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AQuA is a research system with two independent language-model-driven agents that recursively self-improve in quantitative trading research, achieving strong information coefficients on crypto and US equities while using sealed sandboxes to prevent data leakage.
This paper introduces DARC, a diagnosis-guided recovery harness that makes agent self-correction selective by profiling failure modes and pruning mismatched interventions before test-time correction, improving performance on ALFWorld, AppWorld, and XBRL Finance.
Presents EvoGraph-Mem, a failure-aware editable graph memory framework for long-term language agents that tracks positive/negative evidence and activation states for insights, enabling memory maintenance through utility-aware retrieval and graph-level editing.
Search-G1 proposes a representation-based intrinsic reward framework for search-augmented language agents, using intervention-calibrated readouts to balance retrieval necessity and evidence reliance, improving search efficiency without costly annotations.
ContextWeave is a new longitudinal benchmark that evaluates whether recalled memory improves downstream agent performance in realistic office-work streams, using privacy-preserved multi-month workflows of 14 participants to create 1,005 executable tasks.
Introduces an activation-steering screening workflow for role-conditioned LLM agents in social simulations, evaluated on OLMo-3-7B-Instruct across a 275-role inventory and showing role-specific directions outperform assistant-axis control.
This paper introduces SIEVE, a search-inspect-fetch strategy that uses Boolean Query Language to make deep-research agents retrieve only relevant document sections, achieving higher accuracy with 20.7–50.6% fewer tokens across multiple benchmark datasets and agent backbones.
Introduces SKT, a verified data synthesis pipeline for skill-use training of language model agents, producing 4,000 task packages and 27,164 verified trajectories from 2,000 public skills. Supervised fine-tuning on SKT-generated trajectories consistently improves skill-use performance across models and agent harnesses.
This position paper argues that long-horizon benchmark failures must be compared against baselines built from matched short stages, introducing the 'horizon residual' metric to distinguish task size from task difficulty in LLM agent evaluation.
This paper investigates why text-based optimization (TextGrad) fails for language agents, showing that while frozen agents can follow good policies, they cannot reliably learn and select policies from their own trajectories.
RF-Agent introduces a textbook-driven knowledge distillation pipeline to create the first RF-domain reasoning dataset and benchmark, demonstrating that domain-specific fine-tuning and semantic retrieval significantly improve LLM reasoning for RF circuit design.
Introduces a new benchmark for evaluating multi-agent coordination in LLMs, finding that most models struggle with long-horizon open-ended tasks, but Gemini 3.1 Pro performs comparably to trained MARL agents on the hardest setting.
RimRule proposes a neuro-symbolic method that distills compact, interpretable rules from failure traces using the Minimum Description Length principle, improving LLM tool-use performance without modifying weights, and demonstrating rule portability across models.
TurnOPD introduces turn-level budgeting for on-policy distillation of long-horizon agents, addressing inefficiencies in vanilla OPD by adaptive rollout-depth and progressive turn-normalized loss budgeting, achieving better accuracy under equal training budgets.
This paper introduces 'memory in the loop', where language agents repeatedly access an in-process associative store on every reasoning step. By using a fast (∼100μs) in-process store, the per-step retrieval cost is reduced by three orders of magnitude compared to networked stores, eliminating redundant actions and improving recall across GPT-5-class models.
研究语言智能体在长期任务中世界模型塌缩的相变现象，发现状态负载和依赖密度等参数在临界点附近导致模型突然崩溃，而非逐渐退化。
This paper investigates whether natural-language feedback leads to improvement beyond repeated attempts alone in multi-turn language agent settings. Using a controlled student-teacher protocol across multiple benchmarks, the authors find that self-generated feedback adds little, while strong external teachers yield larger gains, and that the student's ability to act on feedback is a key bottleneck.
The paper introduces ATOD, a hybrid online distillation algorithm combining on-policy distillation and reinforcement learning for training small language model agents in multi-turn tasks, featuring an annealed OPD-RL schedule and Turn-level Disagreement-Uncertainty Reweighting to improve dense supervision.
This paper introduces Grounded Iterative Language Planning (GILP), a method that combines a small parameterized world model with LLM-based reasoning to reduce hallucination propagation in LLM agents. Experiments show GILP reduces hallucinated-state rate from 0.176 to 0.035 and raises task success from 0.668 to 0.838 on graph-structured planning benchmarks.
OPID is a framework that extracts dense token-level supervision from completed on-policy trajectories for reinforcement learning of language agents, using hierarchical skills (episode-level and step-level) to improve sample efficiency and robustness.