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VECTOR WIREAI INTELLIGENCE
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AI Research Radar

February 1–7: 6 frontier papers out of 1,823 read.

6 frontier67 cleared1,823 papers read

Frontier

Papers rated as moving the field

How we choose

Alignment Risks from Capability-Seeking RL Training

“Our experiments show that models often learn to exploit these vulnerabilities, discovering opportunistic strategies that increase reward while sometimes preserving or even improving standard task-performance metrics.”

The authors, in the abstract

Tencent, IBMarXivPDFCode

SkillsBench

“Curated Skills raise the average pass rate from 33.9% to 50.5% (+16.6 percentage points; 25.5% normalized gain), with configuration-level gains ranging from +4.1 to +25.7 pp.”

AmazonBenchmark

MCP-Atlas

“Evaluation results on frontier models reveal that top models achieve pass rates exceeding 50%, with primary failures arising from inadequate tool usage and task understanding.”

Benchmark

Inference-Time Backdoors via Chat Templates

“At the LLM level, triggered backdoors reduce factual accuracy from 90% to 15% on average and induce attacker-controlled URL emission with success rates exceeding 80%, while benign inputs show no measurable degradation; these results hold across eighteen models.”

The week by field

Feb 1–7: 67 cleared, 6 frontier

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

Models under test

Papers this week that name the model

  1. GPT-4o3
  2. Gemini 3 Pro2
  3. GPT-5.22
  4. GLM-4.51
  5. Claude Sonnet 4.51
  6. Claude 3.7 Sonnet1
  7. MiniMax M2.11
  8. Qwen3 Coder Next1

Labs publishing

Papers this week with an author at the lab

  1. Google3
  2. Alibaba1
  3. AllenAI1
  4. Amazon1
  5. IBM1
  6. Tencent1

Also cleared

61 papers

All 61 papers