Meta, Duke and UC Davis researchers lift Gemini 3 Flash to 62% on Olympiad math with a better harness
Meta, Duke and UC Davis researchers lifted Gemini 3 Flash from 46% to 62% on Olympiad math by searching for a better harness around the same model. That puts the wrapper on an operator's budget ahead of a bigger model, at least for math.
Reality
- Evidence35
- Adoption
- Not scored
- Hype gap+20
- Incentives
- Not scored
- Confidence40
buildOne report1 publisher Swapping the scaffold moved Claude Opus 4.5 from 42% to 78% on CORE-Bench
The same model scored twice under two scaffolds. A dev.to post uses that gap to argue the dividing line in AI coding is whether the model can run your repo's own commands and read the failure.
Reality
- Evidence45
- Adoption
- Not scored
- Hype gap+18
- Incentives45
- Confidence50
Antigravity gives away five frontier models on a quota Google keeps trimming
Antigravity's free tier includes Gemini 3.1 Pro and Claude Opus 4.6 with unlimited tab completions. XDA reports Opus burns credits about four times faster than Gemini, and Google moved the free tier onto credits in March 2026.
Reality
- Evidence36
- Adoption
- Not scored
- Hype gap+12
- Incentives58
- Confidence40
buildOne report1 publisher A memory agent that keeps the option to stay silent lifts Terminal-Bench 2.0 pass@1 by 8.3 points
An arXiv paper names the failure behavioral state decay and runs a side-car memory agent next to an unmodified action agent, reporting gains of 8.3 and 6.8 points of pass@1 on two long-horizon benchmarks.
Reality
- Evidence52
- Adoption
- Not scored
- Hype gap+18
- Incentives60
- Confidence57
buildConfirmed2 publishers PrismML's ternary build of Qwen3.8 27B keeps 98.2 percent of the full-precision benchmark average on both of the company's inconsistent scorecards, and the loss it does take is concentrated in knowledge and reasoning.
Reality
- Evidence38
- Adoption20
- Hype gap+32
- Incentives72
- Confidence55
buildOne report1 publisher Deep Agents now swaps in apply_patch the moment you name a Codex model
LangChain's new harness profiles set prompts, tool implementations and tool names per model family. The company measures a 10 to 20 point gain on a tau2-bench subset it curated from tasks frontier models have not saturated.
Publishers:langchain.com
Reality
- Evidence45
- Adoption15
- Hype gap+18
- Incentives80
- Confidence55
A $14.34 router matched Opus-5's score on LiteLLM's 21-task benchmark
LiteLLM's own Terminal-Bench run puts a gpt-5.4-mini classifier routing across Haiku, Sonnet and Opus at $14.34 against Opus-5's $19.74 for the same 16 solved tasks. The saving works out at 26 cents a task.
Publishers:docs.litellm.ai
Reality
- Evidence45
- Adoption15
- Hype gap+35
- Incentives80
- Confidence48
buildOne report1 publisher Cached prefixes push 91% of a ReAct agent's LLM time into decode
An arXiv tracing study of Claude Code agents on Gemma and Qwen measured prefix-cache hit rates between 84.6 and 99.5 percent, which moves the serving bottleneck to how long you can keep KV blocks resident between tool calls.
Reality
- Evidence58
- Adoption25
- Hype gap+12
- Incentives
- Not scored
- Confidence55
buildOne report1 publisher Experiential Labs bets its open-source router's traces will train cheaper replacements for rented models
The router picks a cheaper model for work that resembles work it has already seen, and falls back to the strongest one when nothing matches. The same traces that enable that call are the proposed training set.
Reality
- Evidence34
- Adoption17
- Hype gap+41
- Incentives74
- Confidence52