Agents26d ago
DAEDALUS: Bootstrapping Reusable Agent Memory via Self-Generated Tasks and Autonomous Dual-Agent Practice Without Human Oracles
LLM agents operating in novel API or terminal environments frequently repeat identical errors due to an absence of procedural memory. Existing memory bootstrapping approaches rely heavily on curated prompt playbooks or oracle verifiers that require extensive domain foresight. Illuin Technology releases DAEDALUS (arXiv:2610.08048), an autonomous dual-agent framework bootstrapping reusable memory entirely from self-generated practice without human oracles. DAEDALUS pairs an Explorer agent—which probes the execution environment to formulate curriculum-style tasks—with a Solver agent. When the solver stumbles, it extracts candidate heuristics that are committed into a persistent memory bank only after proving their efficacy across repeated verification trials. Across AppWorld, tau^2-bench, and AutomationBench, DAEDALUS boosts average success rates by up to 15.9 percentage points and raises Pass^5 rates by 2.2x over memory-less baselines, with generated procedural memories transferring seamlessly across heterogeneous foundation model families.
Agents26d ago
EVISKILL: Grounding Skill Evolution in Replayable Evidence Enables Continual Procedural Capability Accumulation Without Weight Updates
Continual skill evolution allows LLM agents to autonomously accumulate, refine, and reuse procedural knowledge across multi-turn interactions without updating model parameters. However, prevailing experience-driven reflection mechanisms suffer from two fundamental weaknesses: they decouple synthesized guidance from concrete behavioral traces, and they rely on monolithic global validation that blindly discards valid local corrections whenever an entire workflow fails. Researchers from Tsinghua University and partner institutions introduce EVISKILL (arXiv:2610.05030), an evidence-grounded framework anchoring procedural adaptation to deterministic execution traces. EVISKILL structures raw observations into Replayable Evidence Cards and synthesizes skill edits with explicit contextual lineage. A targeted replay engine verifies edits through isolated re-execution before cross-epoch refinement. Across three interactive benchmarks spanning six LLM backbones, EVISKILL consistently outperforms state-of-the-art reflective baselines, demonstrating durable procedural accumulation.
Agents26d ago
Engram: Pre-Registered Agent Memory Trial Shows Raw Turn Selection Matches LLM Fact Extraction at 3,061x Lower Cost
Conversational memory for LLM agents traditionally divides into two opposing camps: expensive pipeline extraction that distills dialogues into structured factual memory stores versus raw conversational turn retrieval. Literature has reported contradictory findings. Researchers conducted a pre-registered, double-blind controlled study on held-out LoCoMo and LongMemEval benchmarks (arXiv:2609.34227). The investigation reveals that under constrained context budgets, selecting raw conversational turns via a lightweight typed decision model (Jev) is statistically non-inferior to full LLM fact extraction pipelines (95% one-sided bound -3.0 points vs. -5 margin)—while reducing write-time computational costs by a massive 3,061 times. Furthermore, the authors resolve literature contradictions by proving that reranking gains scale inversely with token budgets, while aggressive reranking degrades an agent's correct abstention reliability. All study protocols, data, and code are open-sourced at github.com/ris3abh/Engram.
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