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

Lilian Weng, cofounder of Thinky, published a summary of 35 papers on harness engineering for recursive self-improvement (RSI), arguing that harness improvements will remain essential even as core models evolve. The post highlights proven design trends and optimization literature, including the ACE paper and Meta-Harnesses, signaling Thinky's research direction. Meanwhile, Anthropic expanded Claude's background agent UX with Cowork on mobile and web, and Google's Gemini API added background execution and remote MCP servers for Managed Agents.

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 https://x.com/AIatMeta/status/2074577662840832382 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 https://www.latent.space/p/ainews-microsoft-build-mai-thinking which is nice. We are noted Lilian Weng fans https://news.smol.ai/issues?pattern=lilian%2520weng , 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 https://www.latent.space/p/ainews-all-model-labs-are-now-agent?utm source=publication-search , 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 https://lilianweng.github.io/posts/2026-07-04-harness/ harness-layer-vs-core-intelligence 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 https://arxiv.org/abs/2510.04618 to even more recent trends like Meta-Harnesses https://arxiv.org/abs/2603.28052 , which we have covered anecdotally on AINews https://www.latent.space/p/ainews-its-meta-harness-summer . It surely also provides a hint as to what Thinky is Thinking, beyond just Interaction Models https://www.latent.space/p/ainews-thinking-machines-native-interaction?utm source=publication-search . 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 was Claude Cowork coming to mobile and web https://x.com/claudeai/status/2074525815820169320 , 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 https://x.com/mikeyk/status/2074531605537046953 . Separately, Anthropic extended access to Claude Fable 5 on paid plans through July 12 in a highly engaged announcement from @claudeai https://x.com/claudeai/status/2074548242386178258 , though many users noted the awkward timing relative to weekly limits in reactions from @kimmonismus https://x.com/kimmonismus/status/2074606005963391225 and 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 Machine in their thread https://x.com/SakanaAILabs/status/2074489949529776308 . LangChain echoed the same shift with a new Deep Agents course and an open-source harness project in posts from @LangChain https://x.com/LangChain/status/2074539083204820997 and @hwchase17 https://x.com/hwchase17/status/2074547871194698207 . Google is also productizing this direction: Gemini API Managed Agents added background execution , remote MCP servers , custom function calling , and credential refresh in posts from @ philschmid https://x.com/ philschmid/status/2074533915038027972 and @OfficialLoganK https://x.com/OfficialLoganK/status/2074552932318765376 . Practical agent infra keeps getting more opinionated : There were several notable operator-facing updates: Codex Mobile iOS added task management, filtered diffs, SSH key login, branch comparison, and attachment flows in posts from @Dimillian https://x.com/Dimillian/status/2074396968223211819 and @reach vb https://x.com/reach vb/status/2074400018769793176 ; Hermes Agent added pluggable secrets managers plus native 1Password integration and export of sessions/datasets to formats including private Hugging Face repos in @Teknium’s https://x.com/Teknium/status/2074564207555772912 threads https://x.com/Teknium/status/2074639961727655959 ; Weaviate 1.38 made its MCP server GA with runtime-gated write access, notably allowing MCP SERVER WRITE ACCESS ENABLED to be flipped live without restart in @victorialslocum’s post https://x.com/victorialslocum/status/2074493681403339104 . A more experimental pattern came from @omarsar0 https://x.com/omarsar0/status/2074506169352180108 , 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 Image and previewed Muse Video in announcements from @AIatMeta https://x.com/AIatMeta/status/2074577662840832382 , @alexandr wang https://x.com/alexandr wang/status/2074555909347369105 , and @ tim brooks https://x.com/ tim brooks/status/2074578008296628698 . The notable technical angle is not just image quality, but an explicitly agentic generation loop : planning, web search, tool use, code execution, and self-refinement before rendering. Meta also says performance improves with scaled test-time compute , and that self-refinement behavior emerged during RL rather than being hand-scripted in this follow-up https://x.com/AIatMeta/status/2074587864923250873 . On public evals, Muse Image quickly reached 2 on Image Arena behind GPT Image 2 in Arena’s ranking https://x.com/arena/status/2074581979765539153 , while Muse Video debuted at 3 on Video Arena in another Arena post https://x.com/arena/status/2074591193783320851 . NVIDIA and Cohere both shipped strong audio releases : NVIDIA released Audex , a 30B parameter / 3B active MoE with 1M context for unified text+audio work, summarized by @HuggingPapers https://x.com/HuggingPapers/status/2074384562952749254 and described in more detail by @ weiping https://x.com/ weiping/status/2074537900172050704 . The model’s core claim is preserving text intelligence while adding broad audio generation and understanding via a single MoE backbone. Cohere launched Cohere 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 English in posts from @cohere https://x.com/cohere/status/2074499759616729149 and @JayAlammar https://x.com/JayAlammar/status/2074511963934118282 . Open robotics keeps consolidating around Hugging Face + NVIDIA : NVIDIA expanded its robotics stack into the HF ecosystem by bringing GR00T 1.7 and Isaac Teleop into LeRobot , aimed at open humanoid robotics workflows, in @NVIDIARobotics’s announcement https://x.com/NVIDIARobotics/status/2074380795855147072 and integration guide https://x.com/NVIDIARobotics/status/2074390485251113317 . On the embodied side, UMA showed a strong full-stack robotics narrative: @RemiCadene https://x.com/RemiCadene/status/2074442725814878510 described a prototype built by a small team in 9 months, while the Northstar reveal https://x.com/RemiCadene/status/2074442439142609237 and @psermanet’s safety note https://x.com/psermanet/status/2074512829617491996 emphasized 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 was Liquid AI’s Antidoom https://x.com/liquidai/status/2074494130126811473 , an open-source training method to reduce doom loops where small reasoning models repeat tokens until context exhaustion. The reported reductions are substantial: LFM2.5-2.6B from 10.2% → 1.4% and Qwen3.5-4B 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 @helloiamleonie https://x.com/helloiamleonie/status/2074498103982408044 and @LiorOnAI https://x.com/LiorOnAI/status/2074547819114086561 . 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-A9B compression work got strong attention via @omarsar0 https://x.com/omarsar0/status/2074543978129793462 : compressing a hybrid MoE parent model while preserving reasoning, coding, long-context, and agentic quality, with roughly 2x server throughput and 1M-context concurrency on H100 rising from 1 request to 8 . On the tooling side, Nsight Python 1.0 launched in @HagedornBastian’s post https://x.com/HagedornBastian/status/2074509770342445375 , making GPU perf analysis scriptable in Python. Unsloth also shipped GGUFs for DeepSeek-V4-Flash , plus export to NVFP4/FP8 and speedups for GRPO and MoEs in @danielhanchen’s update https://x.com/danielhanchen/status/2074510444778463331 . Agent RL and verification are getting more specialized : @cwolferesearch https://x.com/cwolferesearch/status/2074558199819067606 highlighted how GRPO-style normalization is being adapted for agentic RL at the task or environment level to handle higher reward variance in multi-turn environments. Separately, @omarsar0 https://x.com/omarsar0/status/2074556579580711050 flagged a training-free verifier paper from Stanford/NVIDIA/Berkeley that reads calibrated continuous scores off scoring-token logits, posting strong numbers across Terminal-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 https://x.com/danburonline/status/2074429991576650014 , @paul cal https://x.com/paul cal/status/2074388528243310976 , and @scaling01 https://x.com/scaling01/status/2074432865794679235 , who argued the vectors are causal largely by construction under the Jacobian-lens definition. A useful historical reference came from @jacobandreas https://x.com/jacobandreas/status/2074487546692735002 , pointing readers back to the original Jacobian lenses paper. The stronger technical takeaway is cross-model structure, not consciousness rhetoric : @eliebakouch https://x.com/eliebakouch/status/2074532904009421260 computed CKA similarity on J-lens geometry across 38 open models and found surprisingly universal layer/depth organization, even across unrelated families like Llama and OLMo . Anthropic and Neuronpedia also released J-lens weights for open models , noted in this follow-up https://x.com/eliebakouch/status/2074537985102565795 . In parallel, Goodfire introduced Block-Sparse Featurizers for multidimensional concepts in activations, arguing many vision concepts are inherently 2–4 dimensional blocks rather than single directions, in their thread https://x.com/GoodfireAI/status/2074634702737281303 . 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 Arena https://x.com/arena/status/2074484787663052849 placed Claude Sonnet 5 Thinking at 6 , with strongest signals in confirmed task success and bash usage, but still with uncertainty around steerability. Artificial Analysis launched Harvey LAB-AA , a legal-agent benchmark over 120 private legal tasks across 24 practice areas , where Claude Fable 5 led at 14.2% all-pass rate ; Claude Opus 4.8 and GLM-5.2 tied at 7.5% , with GLM hitting that at roughly ~6% of Fable’s cost per task in their release https://x.com/ArtificialAnlys/status/2074541975186165887 . 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, in this ICML post https://x.com/GoogleResearch/status/2074384746076135575 . DeepMind also launched Predicting the Past , grounding Gemini in Aeneas and Ithaca for Greek/Latin historical analysis via plain-English interactions, in their thread https://x.com/GoogleDeepMind/status/2074513661750546762 . 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 https://x.com/johnjnay/status/2074485345593245833 . Top tweets by engagement Claude access / product rollout : Claude Cowork on mobile and web https://x.com/claudeai/status/2074525815820169320 and Fable 5 access extended through July 12 https://x.com/claudeai/status/2074548242386178258 were the most-engaged technically relevant product announcements. Open-source developer program : @ClaudeDevs offering 6 months of Claude Max 20x for open-source maintainers https://x.com/ClaudeDevs/status/2074570404035993780 drew massive engagement and is likely to matter for tool adoption in OSS ecosystems. Meta media generation : Muse Image launch https://x.com/AIatMeta/status/2074577662840832382 and Arena’s 2 ranking for Muse Image https://x.com/arena/status/2074581979765539153 were the biggest multimodal product stories. Reasoning reliability : Liquid AI’s Antidoom release https://x.com/liquidai/status/2074494130126811473 stood out as the day’s highest-signal training technique post. Interpretability : Cross-model J-lens universality across 38 open models https://x.com/eliebakouch/status/2074532904009421260 was 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 https://www.reddit.com/r/LocalLLaMA/comments/1uoozt4/new open model from tencent hy hy3 295b total 21b/ Tencent released the non-preview Hy3 open model collection on Hugging Face https://huggingface.co/collections/tencent/hy3 , described as a 295B -parameter MoE with 21B 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 as Apache 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 reported 295B total / 21B active MoE-style configuration, commenters suggested it could be relevant for “high-end home setups” if inference formats such as GGUF become available.There was early speculation that HY3 could become an alternative to Qwen and MiniMax models in local/open-weight workflows, but commenters were waiting for quantized releases and independent testing before drawing conclusions. Keep reading with a 7-day free trial Subscribe to Latent.Space to keep reading this post and get 7 days of free access to the full post archives.