arXiv AI
Jul 24
Delivery, Not Storage: Cue-Anchored Working Memory as a Harness Property for Coding Agents
arXiv:2607. 20972v1 Announce Type: new Abstract: Coding agents ship with one kind of memory: documents.
Minimal Recurrent Behavioral Memory for Imitation under Partial Observability
Related stories
arXiv AI
2d ago
Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies
arXiv:2610.00982v1 Announce Type: cross Abstract: Vision-language-action (VLA) models struggle on history-dependent manipulation tasks, where the current observation alone does not determine the acti...
arXiv AI
Jul 14
Context by Distinct Information: An Auditable Dirichlet-Process Working Memory for Long, Redundant Context Streams
arXiv:2607. 10441v1 Announce Type: cross Abstract: Context engineering decides what information a model carries forward, and current designs meter it in tokens: compressing the past into a bounded recurrent state, keeping a key-value entry for every token, or imposing a fixed budget through a window or eviction rule.
arXiv AI
Sep 4
Learning What Not to Forget: Long-Horizon Agent Memory from a Few Kilobytes of Learning
The paper introduces LRE (Learned Relevance Eviction), a lightweight, CPU‑only, language‑model‑free scorer that learns which parts of an agent’s interaction history are task‑critical and preserves them verbatim. In experiments, LRE matches or surpasses baseline eviction policies on accuracy‑cost trade‑offs, recovers 93% of full‑history accuracy, reduces worst‑case prompt size by 52%, and outperforms dense and token‑pruning encoders in conversational memory while being 295–1569× smaller. The method also achieves superior budgeted answer quality on LoCoMo reading and can be trained annotation‑free, recovering 95% of supervised scorer performance.
arXiv Machine Learning
4d ago
Emergence, Not Bandwidth: Physical Coupling and the Limits of Learned Multi-Agent Communication
arXiv:2609.34373v2 Announce Type: replace Abstract: Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message s...
arXiv AI
Jul 3
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
arXiv:2607. 02255v1 Announce Type: new Abstract: Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see.
arXiv Machine Learning
Sep 2
Does Imitation Learning Preserve Temporal Robustness in Dexterous Manipulation? An Expert-Learner Comparison Across Task Execution Speeds
arXiv:2609.01453v1 Announce Type: cross Abstract: Dexterous manipulation policies learned by imitation are typically evaluated for robustness to variation in scenes, objects, or instructions, but the...
arXiv AI
Jul 3
Procedural Memory Distillation: Online Reflection for Self-Improving Language Models
arXiv:2607. 01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifier and updates the policy from that episode-level signal.
arXiv AI
Sep 7
Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents
arXiv:2609. 04875v1 Announce Type: cross Abstract: Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every serving API, a KV cache.
arXiv Computation and Language
Sep 22
Replay-Gated Neural Execution: Decoupling Persistent Behavioral Specifications from Neural Realizations in Frozen Language Models
arXiv:2609.22243v1 Announce Type: new Abstract: Input-conditioned neural interventions raise a runtime question: what persists when one behavioral specification admits multiple actions whose validity...
arXiv AI
Sep 7
Substrate-Aware AI Agents: Execution Context as a First-Class Input
The paper introduces the concept of substrate blindness, where AI agents lack execution context in their planning. By providing a 128 MB RAM and 10 s wall‑time contract to large language models, the authors show that agents generate code that uses less memory, runs faster, and incorporates structural changes such as bounded blocking and in‑place buffers. Across three leading models, contract disclosure improved resource usage and correctness, demonstrating that minimal execution contracts can guide agents to produce more efficient programs.
arXiv AI
Sep 24
Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents
The paper introduces Just-in-Time Memory (JitMem), a system that defers memory curation until a task is read, allowing a curator to synthesize task‑specific memory payloads based on the current query. Unlike traditional write‑time curation, JitMem retains raw trajectories and trains the curator using immediate task success, avoiding long‑horizon credit‑assignment issues. Experiments on ALFWorld, WebShop, and τ²‑bench show JitMem consistently outperforms both no‑memory agents and existing write‑time memory methods, with improvements of up to 16.3 absolute success‑rate points. whyItMatters":"By curating memory at read time, JitMem enables more effective, task‑adaptive recall that directly improves agent performance across diverse benchmarks."