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The paper presents MFM, a multimodal filesystem memory architecture that combines episodic, spatial, semantic, procedural, and working memory to help long-horizon embodied agents reuse experience, achieving improved performance on Minecraft and computer-use benchmarks across three base models.
Kolab 介绍了一个用于评测人机协作能力的新基准，包含来自40个职业的50位专业人士在115小时以上真实长时程工作流中与AI代理协作的屏幕录像及质量判断，发现前沿模型普遍高估协作质量且难以区分高质量与低质量协作。
LLM-based agents work better with structured data, as demonstrated by Harvey's context graphs for long-horizon legal tasks on the Legal Agent Bench; the author notes DAIR.AI is exploring similar approaches for research tasks.
本文提出 Contextual Credit Policy Optimization (CCPO)，一种无需价值critic的步级信用分配方法，通过上下文相似度加权的同伴比较与未来进度信号，为LLM智能体在长程任务中的强化学习提供更精细的优势估计，在ALFWorld和WebShop上显著优于GRPO。
LHTB 2.0 介绍了一个扩展的长时程终端基准，包含 77 个跨 13 个领域的任务，通过自动化任务构建流水线和密集进度阶梯来衡量 AI 代理在长达三小时工作流中的持续进展，GPT-6-astra 以 0.69 的密集平均奖励领先。
评论介绍了 UCLA 与 Adobe Research 的 Tev-Gym 工作：通过构建可验证的长任务环境与稠密中间进度奖励来训练长时程 Agent，Tev-27B 在 ALE-CM 计算与数学子集上以 Pass@5 比肩 GPT-6 Astra 与 Claude Opus 5，Tev-320B 则将 GLM-5.3-Flash 在 Terminal-Bench 4.0-CM 上的 Pass@1 从 29% 提升至 37%。
SERO 提出通过在真实策略状态上训练可执行的 oracle 续段（并按策略可达性加权）来改进长程 agentic 强化学习，且推理时无需额外开销；在 ALFWorld 与 WebShop 上验证了其有效性。
Doc2Harness 提出了一个两阶段基准，直接评测 LLM 构建长周期编码代理 harness 的能力；在 19 个前沿模型中，GPT-6Astra 以 53.05% 的通过率领先，而表现最弱的模型仅为 27.44%。
The paper introduces TerminalHorizon, an automated data-synthesis engine that generates long-horizon terminal agent trajectories by reconstructing environments around real-world professional tasks, then fine-tunes Qwen3.5-27B to obtain TerminalHorizon-27B, achieving gains across 12 coding, agentic, and reasoning benchmarks.
本文提出层级子任务执行（HSE）框架与 RoboCombine 基准，研究具身操作中技能重组的组合泛化极限，指出失败源于「起始状态差距」，并提出 PRTI 轻量后训练策略以提升跨子任务边界的执行成功率。
ProgAgent 提出一个完整的程序化工具调用（PTC）训练框架，通过可验证的长时程任务合成、轻量级 PTC 运行时和两阶段训练配方，显著优于同规模基线，在 LOCA-bench 64k token 状态下达 30.7% 通过率而基线为零。
A practitioner details the patterns built to keep fleets of LLM agents alive on multi-hour jobs (heartbeats, epoch fencing, intent/done records, parent audits) and explains why failures like credential delivery, workspace resets, and missing dead-man switches still cause silent agent deaths, asking the community for concrete fixes.
TaReD 提出一种工具感知的递归分解方法，通过按功能关系将工具组织成能力层级，让智能体按需发现工具并将复杂长时程任务递归分解为子任务树，实验显示端到端任务成功率最高提升 40 个百分点。
AgentHorizon introduces a benchmark of 1,373 computer-use tasks with human-recorded trajectories to evaluate agentic judges on long-horizon, multi-application tasks. The study finds GPT-5.5 achieves 80.9% balanced accuracy as the best agentic judge, and reveals that tool use helps some models but hurts open-weight models.
pmtui 是一款终端工具，用于管理多个 AI 智能体并处理长时程任务，通过在智能体之外维护循环、每次根据目标和最新状态重建提示词来避免任务漂移。
The paper introduces Trajectory-Anchored Pruning (TAP), the first structural pruning framework for RL-trained long-horizon agents, which couples channel pruning with trajectory-anchored on-policy recovery. With 60% of FFN channels removed, TAP retains 99.2% and 88.0% of dense 7B agents' task success on ALFWorld and WebShop while cutting GPU time per successful task by roughly 22% and 17%.
FC-SWE introduces failure-conditioned reinforcement learning for software engineering agents, using failed patch verifier feedback as context for recovery trajectories. On SWE-bench Verified with Qwen3.5-4B and SWE-agent, it achieves 41.7% Resolved@1 versus 38.9% for GRPO, and reaches 70.7% Resolved@11 at test time.
A practitioner discussion exploring whether long-running AI agent failures stem from model capabilities or from the scaffolding around them, highlighting error compounding, context pollution, and weak self-correction as key failure modes.
The paper introduces ASCENT, a method for online agentic test-time training that lets an LLM agent improve its own weights across tasks by self-distilling verified execution trajectories into persistent LoRA updates, evaluated on ALFWorld, WebShop, and AppWorld.
The paper introduces Generalized Implicit Temporal Abstraction (GITA), a method for offline goal-conditioned reinforcement learning that trains a single k-conditioned value function across multiple temporal abstraction scales, resolving the trade-off between long-range value signal and local resolution. GITA outperforms offline GCRL baselines like HIQL and OTA on OGBench, raising average success rates by 25 percentage points over HIQL.