arXiv Machine Learning
Jun 25
Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One
arXiv:2606. 25449v1 Announce Type: cross Abstract: A language model's memory can be worse than having no memory at all.
Dead text or binding clause? Measuring and restoring constraint influence in black-box LLM dialogues
arXiv:2608. 12599v1 Announce Type: new Abstract: Multi-turn dialogues let users revoke constraints as easily as impose them, but revocation does not reliably take effect: models keep enacting withdrawn requirements (occasionally beneath comments asserting their removal), a failure we call \emph{behavioral relapse}, or revocation inertia.
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Hugging Face Trending Papers
Jun 24
Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One
A language model's memory can be worse than having no memory at all. Give a model a memory that kept a wrong conclusion but dropped the work behind it, and it emits that stale value as a confident answer; give the same model an empty memory and it abstains.
arXiv AI
Jul 22
Binding Drift in Multi-Step Tool-Augmented Agents
arXiv:2607. 18316v1 Announce Type: cross Abstract: Tool-augmented language-model agents execute multi-step workflows over external systems, resolving an entity once and then acting on it across subsequent steps.
arXiv AI
5d ago
Governed Persistent Memory: Source-Bound State Semantics and Fail-Closed Release for Long-Horizon Agents
arXiv:2608. 12476v1 Announce Type: new Abstract: Long-term agent memory is usually treated as select--store--retrieve, but retrieval does not decide whether contradictory, superseded, retracted, deleted, or stale records may support an outgoing claim.
arXiv AI
Aug 7
Runtime Observability for Heterogeneous Attention Memory
arXiv:2608. 05863v1 Announce Type: new Abstract: Modern models no longer keep a plain KV cache: latent caches, learned sparse selectors and recurrent states each carry the model's memory in a different form, and each fails differently under compression.
arXiv Machine Learning
1d ago
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 2
Mnemosyne: Agentic Transaction Processing for Validating and Repairing AI-generated Workflows
arXiv:2607. 00269v1 Announce Type: new Abstract: LLMs, solvers, and agent teams increasingly generate workflow actions, repairs, and plans, but a generated action may be syntactically valid yet stale, infeasible, conflicting, or destructive of the evidence that triggered a repair.
arXiv AI
Jul 9
Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
arXiv:2607. 07405v1 Announce Type: new Abstract: Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully.
arXiv Machine Learning
Aug 5
Resume Means Resume: A Machine-Checked Conformance Contract for Checkpoint, Interrupt, and Resume Semantics in Workflow Persistence Layers
arXiv:2608. 03836v1 Announce Type: new Abstract: A framework that persists execution state so a run can be interrupted, survive a crash, and continue must decide what a resume means for effects that already fired.
arXiv Machine Learning
Aug 6
Wrong Design Intent Can Be Worse Than None: A Derangement-Control Diagnosis of Header Conditioning in CAD Program Completion
arXiv:2607. 23191v2 Announce Type: replace Abstract: Fine-tuned code LLMs are often conditioned on a design-intent header to steer parametric CAD generation, but whether the model reads that header's content has been tested neither under execution-level scoring nor with a causal control.
arXiv AI
5d ago
Large Language Models Can Follow Instructions, But Not Many at Once: Phase Transitions in Compositional Constraint Satisfaction
arXiv:2608. 12426v1 Announce Type: new Abstract: Large language models are increasingly deployed in settings that require simultaneous adherence to multiple explicit constraints - reasoning structure, safety boundaries, output schemas.
arXiv AI
Aug 11
AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts
arXiv:2601. 22758v2 Announce Type: replace Abstract: Large language model agents repeatedly encounter related tasks, yet systems that learn from trajectories commit every lesson to one predefined artifact form.