arXiv AI
Read Less, Solve More: Token-Efficient Sparse Reading for AI Agents
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arXiv AI
Sep 24
Memory Control Signals Emerge Before Action in Long Horizon Agents
The paper investigates how long‑horizon language model agents encode memory‑management signals before taking actions. By examining hidden states just prior to each action, the authors find that the model already signals the need for compression and recall, independent of context length or interaction progress, and that these signals vary across model depth. They propose the Preaction Memory with Evidence Retrieval (PaMER) framework, which uses state‑guided compression and selective evidence retrieval to reduce context consumption while preserving task performance.
arXiv AI
Jun 2
ACON: Optimizing Context Compression for Long-horizon LLM Agents
arXiv:2510. 00615v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise records of actions and observations.
arXiv AI
Jul 28
ACM: Agentic Context Management for Long Horizon Tasks
arXiv:2607. 23809v1 Announce Type: new Abstract: Agentic tasks are inherently long-horizon and multi-turn, constantly accumulating context through interactions with the environment.
arXiv AI
Jul 21
Is Progressive Disclosure All You Need for Long-Context Agents?
arXiv:2607. 17598v1 Announce Type: new Abstract: Long-document question answering usually forces a choice between loading the whole document into the context window and bolting on a separate retriever.
Hugging Face Trending Papers
Jun 29
LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via a Proprioceptive Dashboard
Long-horizon tool agents are bottlenecked by how their context grows toward the limits of the context window. Recent systems make context management agent- or system-controlled, but they either learn a compression policy that discards evidence or manage context in a layer the agent never sees.
arXiv Machine Learning
Aug 4
Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents
arXiv:2608. 01285v1 Announce Type: new Abstract: The continued development of LLMs toward persistent and adaptive intelligence increasingly requires long-term memory mechanisms that preserve and reuse information across interactions.