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VECTOR WIREAI INTELLIGENCE
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AI Research Radar

July 17–23: 16 frontier papers out of 1,654 read.

16 frontier155 cleared1,654 papers read

Frontier

Papers rated as moving the field

How we choose

Two-Level Meta-Rubrics for Evaluating Open-Ended Generation

“Evaluating 14 frontier and open-weight models, we find the benchmark genuinely challenging (best score 58.7% from Gemini 3.1 Pro), highly discriminative, and robust to the choice of judge.”

The authors, in the abstract

GAMUT, a Benchmark for Factual Completeness

MetaBenchmarkarXivPDF

KernelBench-Verified

“Under verified single-turn evaluation with seven frontier LLMs, we find that the best-performing model (GPT-5.5) achieves a 0.88x geometric mean speedup, significantly lower than the 1.43x speedup observed under the standard evaluation protocol.”

MetaBenchmark

RoboTTT

“RoboTTT trained with 8K-timestep context outperforms the same model pretrained with 1K timesteps by 62%, suggesting context length as a new scaling axis for robot foundation models.”

NVIDIA

Profile-Graph Memory for LLM Agents

“ProGraph averages 80.1% on MemHop (matching the FullContext reference) and 78.4% on LoCoMo (exceeding FullContext by 11.3pp), outperforming Mem0, A-Mem, HippoRAG, and RAG on both.”

DARWIN

“DARWIN-Guard achieves an average unsafe recall of 91.6% across 12 safety benchmarks, outperforming strong guardrails such as YuFeng-XGuard and Nemotron Guard, while maintaining a nearly 100% pass rate on standard benign datasets.”

Xiaomi-Robotics-1

“Notably, it establishes a new state-of-the-art with a 57.6% success rate on RoboCasa365, surpassing the previous best of 46.6%.”

NavVerse

“Zero-shot experiments with RL, VLA, and modular baselines show that current agents remain far from solving cross-context navigation: end-to-end VLAs obtain the highest zero-shot success, while the modular method provides the strongest safety profile.”

Benchmark

Hidden in Thought

“Our results show that harmful reasoning transfers at both the trace and pattern levels, motivating defences that evaluate reasoning context in addition to final outputs.”

Ring-Zero

“However, we find that naive scaling often suffers from poor readability, token redundancy, and a lack of adaptive reasoning depth.”

Value Leakage

“We find that models are influenced by different types of values, including preferences for morally good outcomes, for the company that developed them, and for some human leisure activities over others.”

The week by field

Jul 17–23: 155 cleared, 16 frontier

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

Models under test

Papers this week that name the model

  1. GPT-5.55
  2. DeepSeek-V4-Pro2
  3. GPT-52
  4. GPT-4.12
  5. DeepSeek-V4-Flash2
  6. Mistral 7B1
  7. Qwen3 32B1
  8. Claude Opus 4.81

Labs publishing

Papers this week with an author at the lab

  1. Alibaba5
  2. NVIDIA3
  3. Google2
  4. Meta2
  5. Microsoft2
  6. AllenAI1
  7. Amazon1

Also cleared

139 papers

All 139 papers