#llm-agents

18 curated events

papersTODAY 04:00 UTC

GRADE Models LLM Agent Runs as Typed Graphs of Execution and Dependency

A new arXiv paper introduces GRADE, a method that represents a single LLM agent run as a typed graph rather than a plain step-by-step trace. Execution edges are derived automatically from the trace, while dependency edges are provided separately, with each one carrying a graded observation. The authors argue this extra dependency layer captures what each step relied on, information that ordinary traces omit.

agents/llm-agents/research/graph-representation

papersTODAY 04:00 UTC

Hybrid Dependency-Aware Framework for LLM Agent-Based Oracle-to-PostgreSQL Migration

The paper argues that using large language models for database migration is often reduced to a straightforward code rewrite, which overlooks the mixed SQL and PL/SQL objects found in enterprise Oracle environments. It proposes a dependency-aware approach that decomposes the migration into subtasks and dynamically creates LLM agents to handle them, taking execution order, complexity, and validation requirements into account.

llm-agents/database-migration/oracle/postgresql

papersTODAY 04:00 UTC

SkillAtlas: An Attack Trace Library for Agent Skills

Researchers present a library of attack traces aimed at reusable skills for language-model agents. The work argues that risks in agent skills surface through model decisions, user context, tool calls, and execution feedback rather than through fixed signatures or a single sandboxed run, which limits existing static and dynamic analysis methods. The library is intended to help catalog and study these behaviors.

agents/safety/research/llm-agents

papersTODAY 04:00 UTC

arXiv paper asks whether LLM agents can manage long-horizon physical tasks

A newly posted arXiv preprint examines whether large language model agents can autonomously carry out long-horizon physical tasks, which require continuous observation of the environment and consequential actions. The authors frame the question around self-adaptive physical AI, where agents are expected to operate with limited or no human oversight. The abstract indicates a research analysis or position piece rather than a released system.

agents/llm-agents/robotics/research

papersTODAY 04:00 UTC

DynSTEER: Dynamic Stage-wise Evaluation and Review for LLM Agents

A new arXiv paper introduces DynSTEER, a framework for evaluating large language model agents that operate over long-horizon tasks. It targets gaps in existing evaluation methods, which typically judge only final outcomes and struggle to pinpoint where errors occur. The approach combines stage-wise trajectory assessment with review during execution rather than after the fact.

agents/evaluation/llm-agents/research

papersTODAY 04:00 UTC

MetaTool-Enhanced ROS Framework Targets Long-Horizon Instability in Open-Source LLM Agents

A new arXiv paper addresses the tendency of open-source large language models to reason inconsistently and act inefficiently over long tasks when used inside agentic robotics stacks. The authors propose pairing a MetaTool component with the Robot Operating System so that LLM-driven agents can sustain coherent planning and execute actions more reliably. The work focuses on human-robot interaction settings where such instability has limited practical deployment.

agents/open-source/robotics/llm-agents

papersTODAY 04:00 UTC

LIMBO: Inference-Time Memory and Budget Optimization for Lifelong LLM Agents

A new arXiv paper introduces LIMBO, a method that optimizes memory use and compute budgets at inference time so LLM agents can keep learning new tasks without losing earlier skills. It builds on experience replay, which feeds past interactions back into the agent, and targets the cost and context limits that arise as agents run in long, evolving workflows. The work is positioned as a general approach to lifelong capability retention for deployed agents.

agents/memory/llm-agents/research

papersTODAY 04:00 UTC

arXiv Paper Proposes Adaptive Harness for Long-Horizon Clinical LLM Agents

A new arXiv preprint introduces Asclepius, a harness designed to keep LLM agents stable during long clinical tasks rather than short single-step prompts. The authors evaluate it in a Clinical Environment Simulator where an agent handles extended workflows under resource contention, arguing that current benchmarks miss this class of failures. The work targets reliability problems that appear only in hours-long deployments.

agents/healthcare/benchmarks/llm-agents

papersTODAY 04:00 UTC

Elo-per-token Analysis Measures Test-Time Scaling in LLM Agents

A new arXiv paper proposes an Elo-per-token method to assess how LLM agents spend test-time compute while revising answers, calling tools, exploring options, and deciding when to stop. Because agents allocate that compute adaptively, conventional measures struggle to capture how their performance scales, which the authors aim to address with their token-level rating approach.

agents/llm-agents/test-time-compute/benchmarks

papersTODAY 04:00 UTC

arXiv Report Critiques Transient Planning in LLM Bioinformatics Agents

A revised arXiv technical report argues that most existing large language model agent systems for bioinformatics are built around delivering final answers, leaving planning, tool invocation and code execution as short-lived steps. The authors contend this design does not hold up for long-running tasks, though the posted abstract is truncated. The paper appears in the cs.AI category as a replacement version.

agents/bioinformatics/arxiv/llm-agents

papersTODAY 04:00 UTC

Paper proposes separating persona from execution in auditable LLM agents

A new arXiv paper argues that governed organizations need LLM agents whose persona — prompts, tone and self-presentation — can change freely while their stateful work remains traceable. It notes that a single trust domain cannot satisfy both requirements at once, and proposes a persona-execution separation pattern as an architectural fix. The approach is framed around execution auditability for enterprise deployments.

agents/llm-agents/audit/architecture

papersTODAY 04:00 UTC

SkillLift: Learning Dense Rubrics from Sparse Oracles for Agent Skill Evolution

A new arXiv paper introduces SkillLift, a method for improving the reusable procedural prompts that LLM agents keep as persistent skills, which lets them adapt without retraining model weights. Rather than rewriting skill text directly from execution feedback, the approach derives dense scoring rubrics from limited, costly oracle evaluations to make skill evolution more efficient. The work targets lower evaluation cost during agent skill self-improvement.

agents/llm-agents/rubrics/arxiv

papersSEP 12 04:00 UTC

Ecdysis Method Trains Runtime Harnesses for LLM Agents More Efficiently

A new arXiv paper introduces Ecdysis, a technique for training self-evolving runtime harnesses that govern how LLM agents execute tasks. The authors argue that prior harness-evolution approaches depend on costly iterative search, and their method aims to make this process both more efficient and more effective. The work targets improved agent performance while reducing the compute needed to discover good harness configurations.

agents/llm-agents/training/research

papersSEP 12 04:00 UTC

Self-Evolving LLM Agent Accumulates Skills for Urban Traffic Simulation

Researchers introduce SimSkill, an agent that draws on the idea of cumulative culture to retain and build on skills and knowledge over repeated experiences. The system is designed for the Simulation of Urban MObility (SUMO) traffic environment, where it evolves its own capabilities rather than relying on fixed, hand-crafted policies. A revised version of the preprint is now available on arXiv.

agents/llm-agents/simulation/research

papersSEP 12 04:00 UTC

COBRA-Skills: Bandit-Guided Evolution for LLM Agent Skill Optimization

A new arXiv preprint proposes COBRA-Skills, a method that applies contextual bandit guidance to evolve reusable skills for large language model agents. The approach aims to cut the reliance on expensive execution-based evaluation and large task datasets that limit existing skill optimization techniques. It targets agents that reuse skills distilled from earlier task experience.

agents/llm-agents/reinforcement-learning/arxiv

papersSEP 12 04:00 UTC

RouteRepair: Instance-Level Failure Diagnosis and Targeted Repair for LLM-Designed Routing Heuristics

A new arXiv paper introduces RouteRepair, a framework that diagnoses why LLM-generated heuristics fail on individual routing instances and then applies targeted fixes. The work targets routing optimization in freight, urban logistics, and shared mobility, where good heuristics are needed under tight compute budgets. It is positioned within the growing area of LLM-based automated heuristic design.

llm-agents/optimization/routing/heuristics

papersSEP 12 04:00 UTC

Paper Proposes Task-Agnostic Preprocessing of Environments for LLM Agents

A new arXiv preprint describes a method in which an LLM agent surveys the corpora and tools available in an unfamiliar setting and builds reusable assets such as indices, scripts, or procedural notes before attempting any task. The authors argue that most existing automated adaptation approaches depend on task examples, which limits their generality. The work positions this environment preprocessing as a step toward task-agnostic agent adaptation.

agents/llm-agents/research/arxiv

papersSEP 12 04:00 UTC

Study examines catastrophic forgetting in skill retrieval for LLM agents

A new arXiv paper studies how synthetic data affects the ability of LLM agents to select the right external skill from large repositories. The authors describe a deployed skill router covering 34,396 skills and run a large-scale evaluation of retrieval under limited data conditions. The findings point to catastrophic forgetting as a risk when synthetic data is used for training these routers.

agents/synthetic-data/retrieval/llm-agents