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Next big section is on residuals which has devolved to something extremely extremely cursed. They first compare HyperConnections (learnable write, read, mix operators) against a simplied variant where its just a linear combination of various branches and the output is written to a single branch in a roundrobin fashion. They compare both of these with their own variant GatedResidual: 1) RMSNorm each branch 2) Apply projection via a low rank bottleneck 3) Layer is the input coefficients of the different branches 4) Write uses a single branch level scalar

🔬 Even the top AI agents leave about a third of scientific code repair tasks unsolved within a one-hour budget. Title: ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments URL: 🧩 Overview AItonomy, Qwen, and PhAI-Labs built ScienceIDE, infrastructure that turns 27 scientific codebases, spanning astrophysics to ocean modeling, into executable environments AI agents can actually learn from. It ships 64 environments and 2,812 tasks. ⚙️ Problem it solves Scientific code encodes decades of expertise, but fragmented toolchains and undocumented numerical conventions make it hard to turn that knowledge into reliable learning experience. 🛠 Methodology "Scientific checks" comparing numerical tolerances and conserved quantities validate task correctness, across 7 task categories like repair and implementation. For RL, a "truncation masking" trick prevents unfairly penalizing trajectories that hit the budget cutoff. 📊 Results Under a one-hour budget, Fable 5.1 leads the agent leaderboard at 67.1%, with Astra at 63.1%. Models trained on ScienceIDE experience (PhAI-IDE) gain up to +33.33 points on scientific code repair, and RL training lifts reward on LAPS tasks 2.4x. #AIAgents# #AI4Science#

🎙️Facilitation Skills Conflict on a product team isn't a warning sign — the absence of it usually is. In this episode of All Things Product, Petra Wille and Teresa Torres tackle a question Teresa hears constantly: what should a product trio do when collaboration breaks down and disagreement turns into friction? Petra makes the case for something many teams quietly abandoned — the retrospective — and argues that facilitation is a genuine, learnable skill that product people should invest in, not outsource by default. Teresa introduces a framework from academic research on team dynamics: the distinction between task conflict (we disagree on how to do the work) and relationship conflict (something about how we work together puts us at odds). The two require completely different responses, and confusing them is where teams get stuck. Together they get practical about team charters, when to bring in a neutral third party, how to choose that person, joint escalations, and why running an experiment often beats arguing over opinions. If your trio is avoiding hard conversations — or having the same one on repeat — this one's for you. Key takeaways 🔄 Retrospectives still work. Agile may feel unfashionable, but the practice of pausing to ask "how are we working together?" is one of the most valuable things a team can protect time for. 🎤 Facilitation is a skill, not a personality trait. Someone in the organization needs to be good at it — and product people should be learning it, not just relying on an agile coach or scrum master. 📜 Team charters go deeper than mission statements. The powerful version isn't "what's our outcome" — it's working hours, communication preferences, and how each person likes to make decisions. Making implicit norms explicit prevents friction before it starts. ⚖️ Task conflict vs. relationship conflict. Task conflict is disagreement about the work. Relationship conflict is about how you work together. Team charters address the second; shared discovery addresses the first. 👍 Task conflict is good. It's how teams get to better problem solving. If there's none at all, that's likely a psychological safety problem, not a harmony win. 🗣️ Bring task conflict to the team. What looks like two people disagreeing is often a disagreement others share silently — or one someone else on the team can resolve. 🤝 Relationship conflict starts one-on-one. Try to resolve it directly first; HR or mediation is the escalation path, not the opening move. 🎯 Choose your facilitator deliberately. Ask whether context helps or hurts. An embedded agile coach may know too much history to stay neutral; sometimes the product person — or a senior engineer with strong facilitation instincts — is the better pick. 🚪 Leave your ego at the door. A facilitator writes the headline post-its and nothing else. Skilled facilitators even signal role changes physically — Petra describes one who literally switched chairs to mark "team member" vs. "facilitator." 🧪 When it's opinion vs. opinion, run an experiment. Design a test rather than escalating a debate. 📝 Joint escalation as a tool. Having both parties write down the conflict together often shrinks it — and gives leadership something concrete when escalation is needed. Links to Spotify, Apple Podcasts, and YouTube: 📺Youtube: 🎵Spotify: 🍎Apple Podcast: Give it a listen and share your thoughts in the comments below.💬👇

Agent performance isn't just about the model — the harness design matters just as much. TL;DR JIT-Agent dynamically synthesizes, repairs, and evolves agent harnesses (scaffolds) based on task characteristics at runtime. It achieves average gains of +7.7pt on GLM-5.2 and +8.8pt on DeepSeek-V4-Flash, reaching top-1 performance on 8 of 9 benchmarks — surpassing GPT-5.6 and all tested frontier models. Title: Scaling Harness Intelligence via Just-in-Time Harness Evolution URL: Key Points 🧩 Harnesses formalized as machine-learnable artifacts The four-module protocol h = (M, P, A, F) — Memory, Planning, Action, Capability Orchestration — constrains the generation space while remaining expressive enough to represent all 13 harnesses in HarnessFactory. 🎓 Three-stage training: imitation → repair → evolution Stage I learns from teacher-generated harnesses; Stage II trains recovery from execution failures (max 2 iterations); Stage III's Evo-GDPO evolves harnesses that advance the Pareto frontier on performance, latency, and cost simultaneously. 📊 Higher accuracy AND lower cost at the same time On xBench-DeepSearch: score 78→82 (+4pt), tokens 527K→212K (▲60%), cost $0.075→$0.039 (▲48%). Average 36% token reduction versus best fixed harness across all 9 benchmarks. ⚡ Transfers across model families without retraining JIT-generated harnesses outperform ReAct on DeepSeek V4 (+10.2pt avg), Mimo V2.5 (+8.6pt), and Qwen 3.6 (+4.0pt) — no need to retrain the harness generator for each backbone. 🔄 Online evolution continues improving at deployment Streaming mode accumulates successful harnesses across task sequences, outperforming static generation on all three evaluated benchmarks. "Harness intelligence" as a trainable scaling dimension orthogonal to model weights is the key conceptual contribution here. #AIAgents# #LLMScaling#

The gap between a 10x AI user and a mediocre one comes down to two abilities: 1. Domain expertise. You know what a great result looks like because you've done the work for years. This is your ceiling, and it becomes the agent's ceiling 2. Translation. You can pass your taste and standards to the agent through context, references, and iteration The second is a skill, learnable in weeks. The first is a career. No AI will hand it to you.