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Focused Omni-Modal GUI Agents paper proposes FocusGUI adapter for GUI agents
FocusGUI is a lightweight adapter that prioritizes multimodal inputs per step and injects them into a frozen decoder, trained on action supervision alone. Reported gains include +33.2 Type Match and 3.0x Exact Match over the frozen backbone across four omni-modal backbones.
Sage-A1.5, a 35B mixture-of-experts agentic model, is introduced in a research report
Sage-A1.5 builds on A1 with subagent orchestration, progress and evidence verification, and open scientific discovery capabilities. The authors say it is meant as an accessible foundation for the community.
AdaExploreBench benchmarks LLM agents on proactive exploration and adaptation to rule drift
The benchmark hides environment rules and tests whether agents can explore to find them and detect when they change. 9% calibrated accuracy, and supplying sufficient evidence or removing rule drift lifts average performance by close to 10%.
MemPilot proposes on-demand multimodal memory curation for LLM agents
MemPilot uses reinforcement learning to pick between query-agnostic memory retrieval and query-specific curation by LLMs and VLMs, trading off performance, cost and latency across five multimodal agent-memory benchmarks.
OR-License benchmark tests LLM agents on real operations research engagements
Across 11 frontier LLMs in coding-agent harnesses, several beat the expert reference on objective quality, but none matches its Deployment-Adjusted Quality score. 87 against the expert reference of 1.
Meta publishes MemT-1.0, a memory-native model family with Nano and Flash variants
MemT-1.0 internalizes memory into model computation using Gated DeltaNet-2 and Mixture-of-Storage, trained on 34B tokens. At 128K history, Nano reports about 1.86x inference speedup and 66x lower persistent storage than a full KV cache.
Self-HPO framework alternates harness and policy optimization for coding agents
Self-HPO lets a coding agent propose its own harness candidates and then update its policy with reinforcement learning, raising Qwen3.5-35B-A3B's average resolve rate from 57.8% to 64.8% across three coding benchmarks. The paper also reports gains with a stronger external model, such as Opus 4.8, a…
Hugging Face paper introduces CoworkEnv for synthesizing training environments for AI coworker agents
The framework generates executable training environments for AI coworkers and trains PocketWorker models. 9-point gain on multi-turn tasks.
Interlock framework synthesizes long-horizon agent tasks that improve agent pass rates after training
Interlock represents tasks as completion graphs and generates workspaces, requests and rubrics. 16 points.
OpenVLHarness adds persistent multimodal memory and tool use to vision-language models
The harness gives multimodal models a tool capability layer and persistent memory for images, data and web references. 6 point average gains over no-tool baselines across 22 benchmarks and eight backbones, including GPT-6 Luna at lower token cost.
Paper introduces AnyGoal benchmark testing world model generalization beyond expert rollouts
AnyGoal is a benchmark of goal-shifted tasks built from common world-model environments, with an open-source library. The authors report that current world models consistently fail when familiar state and action factors are recombined.
Hugging Face paper introduces HarnessPro for cheaper, stronger AI agent harnesses
HarnessPro evolves agent harnesses without ground-truth tests and outputs lite, medium and heavy variants. 1% versus RHO.