arXiv AI
Jun 3
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.
Benchmarking Task Takeover Across Agent Handoffs
The paper introduces HSC-Bench, a benchmark with 1,039 tasks designed to evaluate how well receiving AI models can take over tasks after handoffs. It measures two key aspects: the ability to recover relevant repository evidence and the ability to continue from established conversational, tool, and file-work states. Experiments with six models show that structured handoff information consistently improves evidence recovery and, for most models, continuation performance, while the best performers differ between the two tracks.
Related stories
arXiv AI
Oct 26
Knowing When to Stop: The Evidence-Action Gap in Coding Agents
The paper introduces the evidence-action gap, where coding agents persist in tasks even when completion is impossible, despite having sufficient evidence that outside help is needed. To quantify this, the authors present ImpasseBench, a benchmark that injects realistic blockers into repair tasks and marks the step at which evidence becomes sufficient. Across eight models and 19,371 trajectories, they find that many agents still exhaust their budget or make unsafe requests after the evidence threshold, but an escalation tool and external monitor can significantly reduce these failures.
arXiv AI
Oct 5
Reach or Solve? Deep Diving into Agentic RL Gains with Checkpoint Handoffs
The paper introduces checkpoint handoff, an evaluation protocol that separates an agent’s ability to reach useful states from its ability to complete tasks in reinforcement learning. By using one checkpoint as a reacher up to a handoff point and another as a solver from the same replayed history, the authors can measure Reach (how often states within a fixed number of actions from success are achieved) and Solve (how often the task is completed from those states). Experiments on TravelPlanner and ALFWorld show that switching the solver from supervised fine‑tuning to RL yields larger gains when RL is used as the reacher, indicating that RL more effectively finds solvable states.
arXiv AI
Oct 15
State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study
arXiv:2610.15407v1 Announce Type: new Abstract: An end-to-end success rate alone cannot distinguish failure to learn an action from failure to apply it as task state changes. We study this distinctio...
arXiv AI
Oct 15
When "Must" Becomes "Maybe": Erosion of Workflow Constraints in LLM Agent Handoffs
arXiv:2608.24569v2 Announce Type: replace Abstract: Large language model (LLM) agents build on one another's work through summaries, plans, and other intermediate artifacts. These records let a downs...
arXiv AI
5d ago
Evidence-Guided Recovery for AI Agents with Ambiguous Tool Outcomes and Partial Execution
The paper introduces an evidence‑guided recovery framework for AI agents that encounter ambiguous tool outcomes and partial execution, such as timeouts or failures. It defines a shared admissibility check for key expiry, authorization timing, dependencies, and residual effects, and a terminal certificate to distinguish actual goal satisfaction from publicly supported safe completion. In a finite study of 37 assignments, the approach achieved 28 certified completions, and in a larger comparison of 58 paired units, evidence‑guided advice certified 30 completions versus 9 for evidence prompting, demonstrating a significant improvement in task completion rates.
arXiv AI
Sep 1
Structured State Reconciliation for Human-AI Task Handover
arXiv:2608.28907v1 Announce Type: cross Abstract: Task handover requires communicating enough current state for a successor to resume work, yet the relevant information is often divided between syste...
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
Oct 7
Beyond Corrected Memory: Execution Consistency in Multi-Agent Systems
arXiv:2610.08101v1 Announce Type: new Abstract: Shared memory coordinates agents' actions, but correct records do not establish that those actions satisfy task requirements. Memory governance and fai...
arXiv AI
Sep 15
Do Not Restart: Residual Completion for Stateful Agent Handoffs
arXiv:2609.13800v1 Announce Type: new Abstract: Routing and cascades reduce tool-agent cost by transferring control across models, but stateful handoffs must preserve accepted choices, realized effec...
arXiv AI
Sep 21
DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement
arXiv:2609.21423v1 Announce Type: new Abstract: Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to...
arXiv AI
Aug 18
AstronOS: A Unified Execution Model and Runtime for Long-Horizon Agentic Systems
arXiv:2608. 16381v1 Announce Type: new Abstract: Agentic systems often organize execution and state around a single conversation, model invocation, or agent instance, even when real work spans many calls and stages.