[AINews] Lilian Weng summarizes 35 papers on Harness Engineering for RSI

a quiet day lets us read some condensed insight

Congrats to Meta Superintelligence on having the top 2/3 image/video models in the world! This would’ve been a candidate for a title story, but unfortunately that is pretty much all the detail we have about Muse Image/Video - no paper, no technical detail whatsoever. Still, this beats the Microsoft MAI models from last month which is nice.

We are noted Lilian Weng fans, so we take notice whenever she drops another research recap, especially rare now that she is a cofounder at Thinky. Today she is thinking about the relationship of harnesses to RSI:

While we have written before about how even Greg Brockman is now quietly endorsing agent/harness engineering, it is refreshing for a respected thinker and neolab cofounder like Lilian to also agree that “Even when many harness improvement[s] get eventually internalized into core model, the need to specify goals and context will not disappear.” Her post breaks out the main proven design trends in harnesses that everyone should know, and then recaps the harness optimization literature, most notably from the well known ACE paper to even more recent trends like Meta-Harnesses, which we have covered anecdotally on AINews.

It surely also provides a hint as to what Thinky is Thinking, beyond just Interaction Models. AI News for 7/06/2026-7/07/2026. We checked 12 subreddits,

[544 Twitters]and no further Discords.[AINews’ website]lets you search all past issues. As a reminder,[AINews is now a section of Latent Space]. You can[opt in/out]of email frequencies!

AI Twitter Recap

Agent Products, Harnesses, and Long-Running Workflows

Anthropic expands “background agent” UX on top of Claude: The biggest product launch by engagement wasClaude Cowork coming to mobile and web, positioning Claude as a task-running background teammate rather than a foreground chat UI. Related posts show the product convergence around a shared home tab and tighter Chat/Cowork integration from@mikeyk. Separately, Anthropic extended access toClaude Fable 5 on paid plans through July 12 in a highly engaged announcement from@claudeai, though many users noted the awkward timing relative to weekly limits in reactions from@kimmonismusand others.Harness engineering is increasingly the center of agent design: Lilian Weng’s new post was widely referenced as reframing recursive self-improvement around the** harness**, not direct weight self-modification; Sakana’s summary connects this to** The AI Scientist**,** ShinkaEvolve**, and** Darwin Gödel Machineintheir thread. LangChain echoed the same shift with a new Deep Agentscourse and an open-source harness project in posts from@LangChainand@hwchase17. Google is also productizing this direction: Gemini APIManaged Agents** addedbackground execution,** remote MCP servers**,** custom function calling**, and** credential refreshin posts from@_philschmidand@OfficialLoganK.Practical agent infra keeps getting more opinionated: There were several notable operator-facing updates: Codex Mobile iOSadded task management, filtered diffs, SSH key login, branch comparison, and attachment flows in posts from@Dimillianand@reach_vb;Hermes Agent added pluggable secrets managers plus native1Password** integration and export of sessions/datasets to formats including private Hugging Face repos in@Teknium’sthreads;Weaviate 1.38 made its MCP server GA with runtime-gated write access, notably allowingMCP_SERVER_WRITE_ACCESS_ENABLED to be flipped live without restart in@victorialslocum’s post. A more experimental pattern came from@omarsar0, using a Dial MCP server so agents can escalate decisions via phone call/SMS/iMessage for human-in-the-loop control.

Model and Modality Releases: Audio, Speech, Robotics, and Media Generation

Meta’s Muse Image/Muse Video push agentic generation into media: Meta Superintelligence Labs launched** Muse Imageand previewed Muse Videoin announcements from@AIatMeta,@alexandr_wang, and@_tim_brooks. The notable technical angle is not just image quality, but an explicitlyagentic generation loop**: planning, web search, tool use, code execution, and self-refinement before rendering. Meta also says performance improves withscaled test-time compute, and that self-refinement behavior emerged during RL rather than being hand-scripted inthis follow-up. On public evals, Muse Image quickly reached**#2 on Image Arena** behind GPT Image 2 inArena’s ranking, while Muse Video debuted at**#3 on Video Arena** inanother Arena post.NVIDIA and Cohere both shipped strong audio releases: NVIDIA released** Audex**, a** 30B parameter / 3B active MoEwith 1M contextfor unified text+audio work, summarized by@HuggingPapersand described in more detail by@_weiping. The model’s core claim is preserving text intelligence while adding broad audio generation and understanding via a single MoE backbone. Cohere launchedCohere Transcribe Arabic**, described as the most accurate open-source Arabic ASR model, under** Apache 2.0**, with emphasis on** dialects**,** code-switching**, and** Arabic-accented Englishin posts from@cohereand@JayAlammar.Open robotics keeps consolidating around Hugging Face + NVIDIA: NVIDIA expanded its robotics stack into the HF ecosystem by bringing GR00T 1.7and Isaac Teleopinto LeRobot**, aimed at open humanoid robotics workflows, in@NVIDIARobotics’s announcementandintegration guide. On the embodied side, UMA showed a strong full-stack robotics narrative:@RemiCadenedescribed a prototype built by a small team in 9 months, whilethe Northstar revealand@psermanet’s safety noteemphasized vertically integrated hardware/software for trustworthy robots.

Training, Inference, and Post-Training Techniques

Liquid AI’s “Antidoom” directly targets reasoning-loop failure modes: One of the clearest technical releases of the day wasLiquid AI’s Antidoom, an open-source training method to reducedoom loops where small reasoning models repeat tokens until context exhaustion. The reported reductions are substantial:LFM2.5-2.6B from 10.2% → 1.4% andQwen3.5-4B from 22.9% → 1% under greedy sampling, with downstream eval gains. The method,FTPO (Final Token Preference Optimization), relabels the loop-triggering token and redistributes probability toward alternatives, summarized well by@helloiamleonieand@LiorOnAI. This is a good example of the field’s recent pattern: removing specific failure modes rather than only scaling parameters.Inference efficiency and compression remain a major frontier: NVIDIA’s** Puzzle-75B-A9Bcompression work got strong attention via@omarsar0: compressing a hybrid MoE parent model while preserving reasoning, coding, long-context, and agentic quality, with roughly2x server throughput** and1M-context concurrency on H100 rising from 1 request to 8. On the tooling side,** Nsight Python 1.0launched in@HagedornBastian’s post, making GPU perf analysis scriptable in Python. Unsloth also shippedGGUFs for DeepSeek-V4-Flash**, plus export to** NVFP4/FP8and speedups for GRPOand MoEs in@danielhanchen’s update. Agent RL and verification are getting more specialized**:@cwolferesearchhighlighted how** GRPO-style normalizationis being adapted for agentic RL at the taskor environmentlevel to handle higher reward variance in multi-turn environments. Separately,@omarsar0flagged a training-freeverifier** paper from Stanford/NVIDIA/Berkeley that reads calibrated continuous scores off scoring-token logits, posting strong numbers acrossTerminal-Bench V2, SWE-Bench Verified, RoboRewardBench, and MedAgentBench and suggesting verification is becoming an independent scaling axis.

Interpretability, Model Internals, and the “J-Space” Debate

Anthropic’s J-space work dominated interpretability discussion, but also drew sharp criticism: The community split between seeing the work as useful mechanistic analysis and objecting to the consciousness framing. Strong critiques came from@danburonline,@paul_cal, and@scaling01, who argued the vectors are causal largely by construction under the Jacobian-lens definition. A useful historical reference came from@jacobandreas, pointing readers back to the originalJacobian lenses paper.The stronger technical takeaway is cross-model structure, not consciousness rhetoric:@eliebakouchcomputed** CKA similarityon J-lens geometry across 38 open modelsand found surprisingly universal layer/depth organization, even across unrelated families like Llamaand OLMo**. Anthropic and Neuronpedia also released** J-lens weights for open models**, noted inthis follow-up. In parallel, Goodfire introduced** Block-Sparse Featurizersfor multidimensional concepts in activations, arguing many vision concepts are inherently 2–4 dimensional blocks**rather than single directions, intheir thread.

Benchmarks, Evaluations, and Domain-Specific Systems

Agent and legal benchmarks continue to expose the gap between “passes many criteria” and “fully solves real work”:Agent Arenaplaced** Claude Sonnet 5 (Thinking)at#6**, with strongest signals in confirmed task success and bash usage, but still with uncertainty around steerability. Artificial Analysis launchedHarvey LAB-AA, a legal-agent benchmark over** 120 private legal tasks across 24 practice areas**, where** Claude Fable 5led at 14.2% all-pass rate**;** Claude Opus 4.8and GLM-5.2tied at 7.5%, with GLM hitting that at roughly~6% of Fable’s cost per task** intheir release. The big message is that models can satisfy many individual rubric items yet still fail to produce acceptable end-to-end deliverables.Research automation and specialized domain systems are broadening: Google promoted** Experience AI Scientist**, a multi-agent system for end-to-end scientific workflows, inthis ICML post. DeepMind also launchedPredicting the Past, grounding Gemini in** Aeneasand Ithacafor Greek/Latin historical analysis via plain-English interactions, intheir thread. On legal AI commercialization,Norm Ai announced a$120M Series C at $1.2B valuation** and described a full-stack “agentic law” setup spanning software plus an AI-native law firm in@johnjnay’s post.

Top tweets (by engagement) Claude access / product rollout:Claude Cowork on mobile and webandFable 5 access extended through July 12were the most-engaged technically relevant product announcements.Open-source developer program:@ClaudeDevs offering 6 months of Claude Max 20x for open-source maintainersdrew massive engagement and is likely to matter for tool adoption in OSS ecosystems.Meta media generation:Muse Image launchandArena’s #2 ranking for Muse Imagewere the biggest multimodal product stories.Reasoning reliability:Liquid AI’s Antidoom releasestood out as the day’s highest-signal training technique post.Interpretability:Cross-model J-lens universality across 38 open modelswas the strongest technical follow-on to the J-space discourse.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Open Model Releases and Inference Efficiency

(Activity: 653):New open model from Tencent Hy: Hy3 (295B total 21B active - apache 2.0)Tencent released the non-preview Hy3 open model collection onHugging Face, described as a295B

-parameter MoE with21B

**active parameters, now under Apache 2.0 rather than the prior restrictive community license. The post highlights that the earlier license reportedly excluded use in regions including South Korea, the UK, and the EU, while top comments point to claimed benchmark gains over HY3-Preview and frame this as potentially relevant for high-end local/home inference setups.**Commenters viewed the Apache 2.0 relicensing as the most important change, especially given Tencent’s recent translation models also using Apache licensing. There was cautious optimism that the reported benchmark improvements may translate to real-world usefulness, but with implicit skepticism until tested outside vendor charts.Commenters highlighted that

Hunyuan/HY3 is now listed asApache 2.0, contrasting it with the prior “community” license that reportedly restricted usage in regions such as** South Korea, the UK, and the EU**. This was viewed as technically important for deployment because Apache 2.0 removes many commercial and geographic usage barriers.Several users focused on whether Tencent’s claimed benchmark improvements over

HY3-Preview will translate into real-world workloads. Given the reported295B

total /21B

active MoE-style configuration, commenters suggested it could be relevant for “high-end home setups” if inference formats such asGGUF become available.There was early speculation that HY3 could become an alternative to

Qwen andMiniMax models in local/open-weight workflows, but commenters were waiting for quantized releases and independent testing before drawing conclusions.

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