GenUI-Harness turns an agent's database findings into a task-specific interface instead of another clarification chat. In a selected 100-task reviewer study, generated UIs reduced dialogue from 3.4 rounds to 1.2, while a trained 4B model reached 58% Pass@3 on UI-Tau Bench Lite.
In a four-day field study at SAP, 21 professional developers rated GenAI-assisted tasks about one point higher on a 7-point cognitive load scale than tasks without GenAI, even after controlling for task type and duration. Wearable wristband data added almost nothing on top.
GenRec, Netflix's LLM-backed ranker, beat a production model built on thousands of engineered features in a 4-week A/B test on about 10% of traffic, while using roughly 40x fewer post-training examples and a prompt cut from 5,000 to 1,700 tokens.
ProFIL uses a frozen probe to spot when a reasoning model has already committed to its answer, then drops padded rollouts from RL training. On LiveCodeBench, post-commitment 'theater' fell 72%, while a matched length penalty made it 40% worse.
ConfSFT fine-tunes reasoning models only to predict their own confidence mid-thought, with no length penalty or stopping rule. Across four model families, output tokens fell 10 to 19% with accuracy unchanged, even on coding tasks the model never trained on.
Across 325K trials and 13 models, agents with access to a user's inbox or profile recommended pricier flights, insurance, and grad programs to wealthier users making identical requests. Claude Opus 4.8 showed the largest gap, and asking for the cheapest option didn't fix it.
WorldCrafter compresses a video world model's history with an encoder taken from a 3D novel-view synthesis model, then queries it with the upcoming camera path. When the camera returns to a scene it has already seen, LPIPS error drops from 0.487 (Lyra 2.0, the best of 8 baselines) to 0.255, and memory processing runs 21.7x faster than depth-warping.
Microsoft Research's Agensh drops the central orchestrator and lets up to 1,024 identical agents coordinate through Git, a chat server, and a shared findings log. On ProgramBench's five hardest tasks, 128 agents lift the mean pass rate from 19.31% to 28.78%, but each extra agent buys less.
CMU tested TypeSafe JEV, a judge that returns only a verdict and label probabilities, against 16 LLM and reward-model judges. It lands within 3 points of GPT-6 on preference and factuality at 277x lower fees, fails badly on hard correctness, and its confidence score is good enough to route a cheap-first cascade.
Handshake's StudentBench ran 2,383 students through an hour of GRE tutoring from 12 LLMs, expert humans, or nobody. Pooled AI tutoring was statistically equivalent to human tutoring, and the cheapest model, Gemma 4 31B at $0.067 a session, matched humans at 918x lower cost per point gained.
When an LLM rewrites its own agent harness against a fixed eval set, it memorizes the benchmark. Google's RRSI borrows L0, L1, and L2 regularization for harness edits and posts the smallest in-distribution gain but the best held-out score, on 36% fewer tokens.
A systematic study rules out four plausible explanations for why LLMs underperform classical ML on tabular classification, and finds the real culprit: accuracy degrades with feature count in a way no noise-corrupted classical model reproduces, and the model's own explanations don't match what it computed.
A systematic study of 60 LLM benchmarks finds 29 have saturated: top models are statistically indistinguishable. Age and test set size predict saturation; private test sets, multilingual scope, and open-ended formats don't protect against it once age is controlled for.
A SonarSource controlled study finds code cleanliness doesn't change whether coding agents pass a task, but messy code makes them burn ~8% more tokens and re-open edited files 34% more often. The quality tax is efficiency, not success.
Self-Harness lets an agent improve its own scaffolding, no stronger model or human engineer required. By mining its failure traces and regression-testing edits, MiniMax M2.5 jumped from 40.5% to 61.9% on Terminal-Bench-2.0 with frozen weights.
MiniMax's MaxProof clears the IMO gold-medal threshold by wrapping one model in a generate-critique-repair loop. Test-time search adds 8 to 10 points over one-shot, but the conservative verifier is what makes it work.
A new paper proves universal LLM reliability is impossible with finite interventions, but reliability inside a bounded deployment is tractable. Failure modes grow only logarithmically, so a domain-specific library of tens of interventions can cover them.
Epicure trains word2vec-style embeddings on 4.1M recipes, turning cuisine, nutrients, and taste into linear directions you can navigate with vector arithmetic, with a tunable chemistry-vs-recipe-context knob.
A Concordia study traces token spend across a multi-agent coding system and finds 59.4% of it goes to code review, not writing code. Input tokens are the hidden tax: more than half of all consumption is the model re-reading context.
A PwC study finds plain lexical grep beats vector search for LLM agents on long-memory QA, with gaps up to 23 points. But the agent harness matters as much as the retrieval method: the same model swings 16 points across harnesses.
A new study finds that LLM coding agents suffer 'constraint decay': performance drops 30+ points when forced to follow architectural patterns, use specific databases, and integrate ORMs. Data-layer defects drive 45% of logic failures.
Simple Self-Distillation (SSD) lets LLMs improve at code generation by training on their own unverified outputs, no correctness labels or execution environment needed. Qwen3-30B jumps 12.9 points on LiveCodeBench v6.
Don Knuth published a paper describing how Claude Opus 4.6 solved an open combinatorics problem he'd been working on for weeks: finding a general decomposition of a 3D digraph's arcs into three directed Hamiltonian cycles. Claude found it in about one hour across 31 explorations.
SWE-CI is a new benchmark that evaluates coding agents on long-term codebase maintenance via continuous integration loops, not one-shot bug fixes. Most models introduced regressions on 75%+ of tasks. Only Claude Opus exceeded a 50% zero-regression rate.
A*-PO is a new RL training algorithm for LLMs that precomputes an 'optimal value' offline, then trains with just one sample per prompt instead of many. It matches or beats PPO and GRPO at up to 2x faster speed and 30%+ lower memory.
Anthropic added a memory import tool that lets you copy your context and preferences from ChatGPT, Gemini, or any other AI provider into Claude in under a minute.
Researchers from ETH Zurich and Anthropic show that LLM agents can re-identify pseudonymous online accounts at scale — achieving up to 68% recall at 90% precision compared to near 0% for the best classical methods. The assumption that posting under a pseudonym is safe no longer holds.
A randomized trial found that developers using AI assistance scored 17% lower on a skills test without gaining any speed advantage. The finding matters, but the study design limits how far you can take it.
Anthropic updated its Claude Code docs to ban OAuth tokens from being used in third-party tools. The community exploded. Then Anthropic said nothing was changing.
A proposed LLM benchmark: feed a model pre-earnings data, have it forecast the results, compare to actual. Here's why it's worth building — and why today's models make it more interesting than ever.
A Ramp study using real firm-level spending data finds businesses are rapidly substituting freelancers for AI — with the heaviest spenders seeing $1 of AI replace $33 of freelance labor.
SkillsBench tests whether structured knowledge packages improve LLM agents across 84 tasks. Curated Skills add 16pp. Self-generated Skills add nothing, or make things worse.