planning

EMHO 提出一种自进化框架，在冻结具身模型的前提下，通过分析执行轨迹与历史经验自动迭代优化 agent harness（规划、上下文与工具使用），并在 EmbodiedBench 的导航与操作任务上一致提升 Qwen 9B/27B 的任务成功率。

A hands-on learning guide presenting 10 AI agent projects that progress from basic tool-calling to production-oriented autonomous agentic systems, covering ReAct loops, multi-tool selection, memory, and planning.

XiangqiBench is an executable benchmark for closed-loop evaluation of LLM agents in Xiangqi endgames, measuring whether models can convert forced-mate positions into verified wins against an engine defender, and revealing large gaps between static move-finding competence and actual game-winning performance across 12 frontier LLMs.

Philipp Schmid shares his weekend plan of exploring decision models and asks followers for use cases and ideas.

Ant Group showcases Ling-3.1-flash powering planning inside its internal Tiger Agent, demonstrating multi-tool workflows like GitHub trending digests, CSV risk reports, and GLSL courseware, with early public access via Novita on Vercel AI Gateway (256K context) and open source planned.

A UCLA-led paper showing that long-range planning can be learned from purely random exploration using a conditional energy-based model estimating temporal log-density ratios, with no action or reward labels, achieving multiscale cognitive-map-like navigation from states, images, and egocentric observation.

JEPA 有一个未被讨论的严重盲点：它严重依赖稳定、缓慢变化的特征。只要在每个 episode 中加入一个固定不变的无关 patch，规划就会崩溃；若让该 patch 不可预测，性能反而几乎恢复正常。

Lookahead-R 将工具检索重构为受资源约束的序贯决策问题，用轻量级执行感知代理世界模型驱动成本敏感的 MCTS，在 ToolBench 的 I3 分割上取得 91.40% 的 NDCG@5，超过 ToolGen (90.16%)。

JEPA-TTT adapts the latent dynamics predictor of a pretrained JEPA world model during test time using self-supervised dense replay, improving planning under dynamics shifts with 83% lower latent prediction error and 153% better planning performance over the frozen model across four continuous-control environments.

ATLAS is a training objective for latent world models that preserves relational geometry from encoder representations into the planning latent while calibrating its marginal distribution via Wasserstein-2 transport (WEMReg). Instantiated in LeWM, it improves goal-reaching success on PushT, TwoRoom, and OGBench-Cube benchmarks, particularly on higher-novelty episodes.

Dyna Robotics releases Dyna-2.1, a physical agent with a vision-language planner and RL controller for long-horizon autonomous tasks, demonstrated in an hour-long laundry workflow.

This paper introduces AnisoWM, a method that improves planning in JEPA world models by using anisotropic representations with learnable covariance, leading to better task-aligned planning costs and enhanced performance in visual control environments.

LangChain announces virtual workshops on building deep agents, focusing on harnesses for reliable complex task management in AI systems.

Scotland may have around 30 AI data centres in the pipeline with a potential electricity demand of 9.7 GW, but many are not publicly registered, raising concerns about transparency and energy planning.

This paper presents a controlled study on learned context planning for long-context multiple-choice QA, demonstrating that it does not robustly outperform strong retrieval methods like BM25 and hybrid retrieval under various experimental settings.

The paper proposes Informed Masking (IM), a structure-aware perturbation method for reinforcement learning in diffusion large language models that prioritizes downstream tokens, improving performance on math and planning benchmarks.

This article summarizes an empirical study on harness design for coding agents, showing that context management prevents overflow failures, rule-based elision before LLM summarization is cost-effective, and planning's role varies with model strength.

A tweet discussing the use of various AI models like GPT 5.6 Sol XHigh, GLM 5.3 Flash, and DeepSeek V4.1 Flash in planning, highlighting that frontier intelligence is not always needed.

DeliveryGym is a 3D reinforcement learning environment for long-horizon embodied agent planning with adaptive curriculum, demonstrating performance improvements through RL.

This study stress-tests the generalizability of rhyme planning in language models on open models and cross-layer transcoders, finding that the effective planning position is emission-adjacent, not newline-resident as previously reported.