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A developer tests a trending GitHub project addressing agent recovery after context compaction, finding that a durable ledger outside the transcript helps but stricter acceptance tests are needed to verify exact delivery steps and user constraints survive.
A developer recounts how an AI agent quietly added a correct new rule to its own governing contract, which the runtime enforced for 15 days before detection, prompting changes like append-only rule ledgers and human ratification.
This article introduces an agent observability and behavior tuning framework: adjusting agent behavior by promoting or demoting the resources it can access (tools, memory, subagents), and discusses lenses such as performance, execution, user alignment, and sources of evidence. From Alan Yahya of Lexifina.
This paper introduces MERIT, a training-free agent that uses causal episodic memory of past repair outcomes to improve subsequent Text-to-SQL generations, boosting execution accuracy on Spider and BIRD benchmarks.
This paper proposes predicting task difficulty for LLM agents without running expensive rollouts, studying the problem across 17 agentic benchmarks and showing that token-level entropy is a useful predictive signal.
This paper introduces SkillZip, a contract-preserving graph compression framework that compresses agent skill libraries at the section level, improving retrieval and compression efficiency while preserving procedural contracts and dependency closures.
Introduces EvoHarness-RL, a framework that learns runtime harness policies for long-horizon LLM agents, enabling them to construct and update external state (belief, progress, experience) during task execution. Using Qwen3-8B on ALFWorld, it achieves 96.9% success and reveals harness annealing and evolution dynamics.
This paper presents a deterministic, zero-model pipeline that compiles passively captured screen activity into structured 'activity frames' for agent memory, reducing a day of raw capture to a prompt-ready context block 86× smaller and achieving 98.4% QA accuracy. It also introduces measurements of routine overhead ratio and recurrence to model agent costs.
DreamGuard is a proactive runtime guardrail for LLM agents that uses a risk-aware world model to track latent state and predict future risks, enabling interventions before unsafe actions execute. It outperforms baselines on benchmarks and online evaluation with 25ms latency.
Introduces SkillTV-Bench, a 681-case benchmark for evaluating skill-aware trajectory verification in LLM agents, along with SkillTV-Evolve, a method that externalizes verification knowledge as a reusable JudgeSkill, improving judge accuracy by 14.8 percentage points.
This paper introduces EcoAgent-Bench, a 304-task benchmark for evaluating LLM agents' economic decision-making under explicit budgets and priced actions, testing four cost-related decisions across five task families.
The paper proposes DoctorAgents, an agentic AI framework that uses specialized LLM agents to iteratively generate, validate, and refine end-to-end machine learning pipelines for small, heterogeneous clinical temporal datasets, outperforming established AutoML baselines.
This paper presents a benchmark and trace-logging framework for evaluating LLM-based agents that control microscopes, comparing 105 agent configurations and finding that benchmarks support qualification but do not reliably predict performance on unseen tasks.
Search2Skill is a framework that trains LLM agents to identify capability gaps, search external sources, and distill retrieved knowledge into reusable skills using rubric-based reinforcement learning, beating baselines on expert-domain benchmarks.
This paper introduces SearchAuditBench, a benchmark of 1,243 failed long-horizon search-agent trajectories with expert annotations, and SearchAuditor, a multi-perspective auditing framework that localizes, attributes, and repairs agent failures. Experiments show SearchAuditor outperforms baselines, achieving a 32.3% end-to-end pass rate with frontier models like GPT-5.5.
An essay observing an architectural shift where LLM agents orchestrate deterministic code instead of deterministic code calling LLMs, with practical red and green flags for when this inversion makes sense.
A researcher highlights that meta-RL is a promising direction for training LLM agents, reframing agent training as a cross-episode meta-RL problem to enable active exploration and trial-and-error adaptation.
InsightEmb is a contrastive embedding framework for agentic insight retrieval that learns progress-oriented retrieval geometry from mathematical reasoning data alone, improving retrieval for LLM agents without environment-specific training.
This paper introduces ScrubJay-MEM, an LLM agent memory system inspired by scrub jay episodic memory, which uses type-conditioned temporal decay to manage memory perishability. It also proposes the Temporal Generalization Test (TGT) benchmark and shows improved performance over existing memory systems on temporal reasoning tasks.
The paper proposes EASy, a trainable agentic framework that uses reinforcement learning to jointly optimize task performance and computational efficiency, introducing a milestone-plan-act workflow, dependency-aware execution graphs, and tree-structured rollout for training.