arXiv AI
Aug 18
Governance at the Boundary: How Agent Decomposition Degrades Policy Compliance
arXiv:2608. 16055v1 Announce Type: new Abstract: Existing agent benchmarks ask whether the agent finished the task.
Handoff Debt: The Rediscovery Cost When Coding Agents Take Over Interrupted Tasks
arXiv:2606. 02875v1 Announce Type: new Abstract: Coding-agent benchmarks evaluate whether a single uninterrupted agent can resolve a repository issue.
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arXiv AI
Aug 25
SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?
The paper introduces SWE Refactor Bench, a benchmark of 20 whole‑repository migrations aimed at testing whether coding agents can autonomously perform long‑horizon stack migrations. It employs a three‑stage evaluation—Migration Audit, Behavioural Tests, and Agentic Verification—to assess both migration completeness and behavioural correctness, revealing that only 5.4 % of runs succeed across all stages. The study shows that agents struggle with migration tasks, especially language rewrites, and highlights the need for more robust coding‑agent capabilities.
arXiv AI
6d ago
Facts Without Rules: Boundary Metadata Collapse in Multi-Agent LLM Handoffs
arXiv:2608.29028v1 Announce Type: new Abstract: Multi-agent LLM systems often coordinate by compressing an upstream interaction into a handoff artifact that downstream agents treat as shared state. W...
arXiv AI
Jul 8
Lingering Authority: Revocable Resource-and-Effect Capabilities for Coding Agents
arXiv:2606. 22504v1 Announce Type: cross Abstract: Coding agents often receive broad tool access for an entire task, even when a resource is needed only for one subgoal.
arXiv Machine Learning
Aug 18
The Working Set of a Coding Agent: Coherence Debt in Repository-Scale Tasks
arXiv:2608. 16630v1 Announce Type: cross Abstract: Repository-scale coding requires an agent to keep tests, imports, configuration, and migration rules consistent within a bounded context window.
arXiv AI
Jul 15
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
arXiv:2607. 13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.
Hugging Face Trending Papers
Aug 4
Fail-Fast, Restart-Smart: Early Failure Prediction and Restart for SWE Agentic Tasks
Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer and exhibit redundant exploration or looping, suggesting that some failures may be detectable before completion.
arXiv AI
5d ago
REVISE: Validity-Guided Recovery for Online Revisions in Agent Workflows
The paper introduces “Revise”, a runtime system that performs validity-guided, fine-grained recovery for online revisions in structured agent workflows. When a revision arrives, Revise intersects the change with recorded data and control dependencies, propagates the impact through the partially executed DAG, stops invalid work, preserves unaffected progress, and recomputes only the affected region. Experiments on real coding‑agent traces and LangGraph/LLMCompiler applications show that Revise matches a latest‑version oracle, reduces model calls by up to 56%, and improves service‑level objective goodput under load.
arXiv Machine Learning
Aug 11
CommitKV: Lifecycle-Aware KV Cache Compression via Commit Transitions for Multi-Turn Agents
arXiv:2608. 07855v1 Announce Type: new Abstract: Multi-turn Reasoning-and-Acting (ReAct) agents accumulate growing trajectories of reasoning, tool calls, and observations.
arXiv AI
Aug 26
When "Must" Becomes "Maybe": Constraint Weakening in LLM Agent Workflows
Large language model agents coordinate tasks via multi‑role, multi‑stage workflows that transform upstream state into intermediate artifacts such as summaries and plans. The study shows that when these artifacts are transformed—through compression, plan assimilation, or other handoff methods—the strict action‑binding constraints on upstream state can be weakened, turning mandatory requirements into optional information. In 1,296 synthetic episodes, direct handoff preserved all safety blockers, whereas transformed handoffs frequently deactivated or forbidden actions, but restoring full state fields or applying downstream verification can recover preservation.
arXiv AI
Aug 26
The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents
The paper investigates the cost–quality trade-offs involved when coding agents switch between low‑cost, low‑capability (LC) and high‑cost, high‑capability (HC) language models during long‑running tasks. By experimenting with different handoff directions, timings, and interfaces—full‑trajectory transfer, compaction, and trajectory removal—the authors find that full‑trajectory escalation recovers less than half of the LC‑to‑HC quality gap while adding significant cost, a penalty they call the handoff tax. Conversely, downshifting from HC to LC yields a more favorable cost‑quality balance, and the optimal interface depends on the direction of the handoff. whyItMatters":"The study quantifies how model handoffs impact both performance and expense, offering guidance for designing more efficient coding agents that balance cost and quality."
arXiv AI
6d ago
Cost-Effective Repository Exploration for Agentic Issue Localization
arXiv:2608.29675v1 Announce Type: cross Abstract: Repository exploration is a distinct and costly stage of coding-agent pipelines: before generating a patch, an agent must identify which repository f...