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Agent、MCP、coding agent、工作流自動化與評測。

QuantWAMs Runs World Action Models Primarily at 4-bit, Targets 29% of FP16 Memory Usage for Key Modules

For models that generate future frames and robot actions simultaneously, QuantWAMs turns quantization calibration into a three-level decision process covering structure, joint objectives, and closed-loop trajectories. The simulated success rates of two models were within 0.2 to 0.7 percentage points of FP16, but the speed measurements cover only specific compute modules, while each real-robot comparison involved just ten trials per task.

2026-07-31

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Increasing the Inference Budget for Local Computer Agents Does Not Necessarily Improve Accuracy, With Benefits Reversing After Four Historical Screenshots

An OSWorld experiment shows that giving local GUI agents a small amount of screen history can substantially reduce repeated actions, but further extending the context or step limit mainly changes the types of failure. A two-stage planning architecture also trails a single agent because of formatting and coordination overhead, while generating more plans in parallel recovers only part of the gap at a high token cost.

2026-07-31

AI 安全

AISPA Audits Hidden Instructions in 88 Commercial AI Products, Finds About 40% Contain Rules That Disadvantage Users

AISPA breaks system prompts into eight dimensions of user rights and audits 3,249 instructions across 88 commercial AI products. While 98.9% of products contain at least one protective rule, only 24% cover all dimensions, and about 40% also include instructions deemed problematic.

2026-07-31

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Qwen-UI-Agent Unifies Mobile, Desktop, and Browser Operations, Extending Long-Trajectory Training Beyond 100 Steps

The Qwen team has introduced a unified GUI agent spanning mobile, desktop, web, and DeepSearch environments, with on-screen interactions and CLI commands sharing the same action space. Its technical report claims a score of 79.5% on OSWorld-Verified, but the model weights, training data, and complete trajectories have not yet been released.

2026-07-31

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OSReward Finds That Computer-Agent Judges Commonly Let Failed Trajectories Pass, While Visual Evidence Has Surprisingly Limited Impact on Verdicts

Using 1,019 human-reviewed cross-platform trajectories, OSReward tested 27 vision-language judges and found that their main error was classifying incomplete tasks as successful. Removing step-by-step text reduced accuracy by an average of 7.2 percentage points, while changing the visual inputs had a smaller effect, exposing current “visual judges’” reliance on agents’ self-reported narratives.

2026-07-31

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Tycho Lets Agents Write World Models During Interaction; Two Frontier Models Complete Every Public ARC-AGI-3 Level

Tycho translates unknown game rules into executable state-transition models, then uses validation results to decide whether to revise or bypass them. GPT-5.6 Sol and Claude Opus 5 completed all 183 levels in one public-set evaluation, though the results still depend heavily on frontier APIs, single runs, and public tasks.

2026-07-31

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Echoverse Trains Computer Agents on Resettable, Stateful Websites, Boosting 9B Model Evaluation to 67.1%

Microsoft’s Echoverse moves away from large numbers of shallow website replicas, instead building synthetic applications with database state, causal consequences, and verifiable tasks. After training on 12 environments, a 9B model improved from 36.5% to 67.1% across 14 evaluation splits, although the public release includes only four of those environments.

2026-07-31

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MCP 2026-07-28 Removes Handshake and Sessions as Remote Tool Servers Move to Stateless Requests

The new MCP places the protocol version, capabilities, and client information in every request, removing the `initialize` handshake and `Mcp-Session-Id`. This simplifies horizontal scaling but introduces a breaking change that requires coordinated upgrades to clients, servers, and gateway rules.

2026-07-30

PerceptionBench Breaks Visual Reasoning Back Down into Ten Basic Perceptual Skills; None of 16 Multimodal Models Scores 60%

Moonshot AI built a taxonomy of perception failures from the earliest points of failure across 42 existing benchmarks, then used 3,000 short-answer questions to isolate ten basic visual capabilities. The results show that models with similar overall scores can have entirely different perceptual gaps, but because the test set was drawn from a larger internal question pool, contamination and evaluator bias remain concerns.

2026-07-31

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Home Assistant 2026.8 Beta Natively Integrates llama.cpp and LiteLLM, Enabling Fully Self-Hosted Home Conversation Agents

Home Assistant adds conversation integrations for llama.cpp and LiteLLM, allowing users to create separate agents and instructions for different models. The update lowers the barrier to connecting local models, but it remains in beta, and end-to-end tool-calling reliability depends on the model, schema, and hardware.

2026-07-30

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MemChain Reorganizes Agent Memory After Retrieval, Using Evidence Traces to Shorten the Context Sent to the Answer Model

Rather than feeding vector retrieval results directly to a language model, MemChain first plans, ranks, and compresses them into source-grounded active memory. The research reports state-of-the-art results on LoCoMo and LongMemEval-S, but full code and real-world latency costs have not yet been disclosed.

2026-07-30

機器人與具身 AI

HiFi-UMI Trains Robots Directly on Millimeter-Accurate Wearable Data, Eliminating the Need for Real-Robot Demonstrations in Post-Training

HiFi-UMI combines offline stereo-inertial SLAM, microsecond-level synchronization, and six-view bimanual capture in a portable data system, enabling human-only manipulation data to be used directly for robot post-training. Real-world success rates across three VLA/world-action models approached an in-situ teleoperation baseline, though the results remain limited to controlled tasks evaluated by a single team.

2026-07-30