arXiv:2607. 12267v1 Announce Type: cross Abstract: Language agents that interleave reasoning and tool use degrade sharply as reasoning chains lengthen, even when each individual step is easy.
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arXiv:2609.14412v1 Announce Type: new Abstract: Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmar...
StepKV introduces a step-aware approach to compressing the key-value cache used during large language model inference, treating reasoning steps as primary units of retention rather than individual tokens. By linking cache entries to the steps that generated them and estimating each step’s utility from trajectory signals, StepKV assigns a combined token‑ and step‑level score to guide pruning. Experiments on multi‑hop question answering and long‑horizon web reasoning show that StepKV maintains accuracy even under tight cache budgets, outperforming token‑level baselines that suffer sharp performance drops.
arXiv:2608. 10676v1 Announce Type: new Abstract: Large language model (LLM)-based search agents answer questions through multi-step interactions with external environments.
arXiv:2602.03053v2 Announce Type: replace Abstract: Multi-Agent Systems (MAS) built on Large Language Models (LLMs) often exhibit high variance in their reasoning trajectories. Process verification,...
The paper introduces MIRAGE, a benchmark of 750 multi‑step decision tasks designed to test autonomous web agents’ investigative abilities across Wikipedia Forensics, Shopping Admin adjudication, and Reddit Moderation. Each task contains a misleading visible context and a hidden context that holds decisive evidence, allowing performance to be broken down into Investigation, Reasoning, and Decision Accuracy, with an added Investigative Hallucination Rate. Evaluation of eight LLM agents reveals three consistent patterns: agents often reach relevant pages but fail to extract decisive evidence, procedural hints improve investigation but not decision accuracy on Wikipedia tasks, and 12.6% of trajectories include fabricated facts.
Agent Zero Memory is a provenance‑aware long‑term memory system for large language model agents that distills user interactions into three parallel memory structures: an episodic timeline, an associative entity‑event knowledge graph, and a semantic, citation‑locked hierarchical documentary memory. Retrieval is performed via an intent gate, source router, and concurrent searches across the three systems, producing integrated, cited answers that exclude fabrication and require evidence the reader has opened. The system achieves state‑of‑the‑art performance on LongMemEval (95.60%) and LoCoMo (93.60%) while offering a favorable accuracy‑cost‑latency trade‑off across multiple backbone LLMs.