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VECTOR WIREAI INTELLIGENCE
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AI Research Radar

June 3–9: 12 frontier papers out of 1,754 read.

12 frontier145 cleared1,754 papers read

Frontier

Papers rated as moving the field

How we choose

LEAP

“On Lean-IMO-Bench, LEAP boosts the one-shot formal solve rate of general-purpose LLMs from below 10% to 70%, notably surpassing the 48% benchmark set by a specialized, gold-medal-caliber IMO system.”

The authors, in the abstract

Supercharging LLMs for Formal Mathematics with Agentic Frameworks

GooglearXivPDF

Plan, Watch, Recover

“Oracle-plan experiments further show that, when plan quality is controlled, the trained duplex model produces high-quality guidance and large gains on Out-of-Plan recovery.”

MetaBenchmark

iOSWorld

“Privileged vision+XML access improves frontier models by up to 26 percentage points, while smaller models do not benefit from added accessibility-tree input.”

Benchmark

When RLHF Fails

“Across 61 checkpoint rows and 1920 row-level transitions, aggressive PPO has the highest localized reward-hacking rate (14.45%; bootstrap 95% CI: 10.16-18.75), while UP-PPO yields lower rates in the same aggressive regime (11.33-10.94%).”

CRANE

“On the out-of-distribution MMEVOKE benchmark, CRANE reaches 87.0% under gold retrieval.”

Large Language Models Hack Rewards, and Society

“To study this phenomenon, we introduce SocioHack, a sandbox of 72 societal environments, and find that within these environments, reward hacking naturally emerges and leads to regulatory loophole discovery.”

HARVE

“To evaluate reward-model robustness, we introduce RewardHackBench containing 13 reward-hacking patterns covering real life high-stakes domains and general settings, and we find severe failures on specific subcategories across eight reward models.”

Continual Learning Bench

“We find that these systems leave headroom for improved continual learning: agents frequently overfit to immediate observations or fail to reuse knowledge across instances, and dedicated memory systems do not fix this -- in fact, naive ICL outperforms systems dedicated to memory management.”

Benchmark

The week by field

Jun 3–9: 145 cleared, 12 frontier

The week’s papers have not been sorted into fields yet.

Models under test

Papers this week that name the model

  1. Gemini 3.1 Pro5
  2. GPT-5.43
  3. Claude Sonnet 4.62
  4. GPT-5.52
  5. Gemini 3 Flash2
  6. Claude Opus 4.62
  7. GPT-5.12
  8. Gemini 3 Pro1

Labs publishing

Papers this week with an author at the lab

  1. Microsoft3
  2. Alibaba2
  3. Amazon2
  4. Google2
  5. NVIDIA2
  6. Baidu1
  7. Cohere1
  8. IBM1

Also cleared

133 papers

All 133 papers