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VECTOR WIREAI INTELLIGENCE
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AI Research Radar

August 7–13: 11 frontier papers out of 2,028 read.

11 frontier163 cleared2,028 papers read

Frontier

Papers rated as moving the field

How we choose

C³PO

“Through attention probes, we find 86-95% of failures stem from modality dominance: models commit to one modality while ignoring contradictory evidence, concentrating 87-95% of attention on text.”

The authors, in the abstract

Evaluating Cross-Modal Composition and Counterfactual Performance in Omnimodal Models

MicrosoftarXivPDF

Motif 3

“Across a broad evaluation suite, Motif 3 demonstrates competitive performance against leading open weight models, including strong results on long-horizon agentic tasks, mathematical reasoning, scientific knowledge, and hallucination-sensitive evaluation.”

FrontierFinance

“Evaluating frontier models and agent systems under a common harness restricted to publicly available data, we find that the tool harness, not the model alone, strongly shapes quality and efficiency; that Samaya's in-house system leads at 56.0%, ahead of the strongest frontier model (Claude Fable 5, 49.2%) at roughly 2.2x lower cost; and that the best open-weight model (Kimi K3, 46.4%) nearly matches the best proprietary model at 4.5x lower cost.”

Benchmark

ComplexityWorld

“Under direct inference, all evaluated models ex- cept GPT-5.6-Sol remain below 40% verifier ac- ceptance rate (VAR), while GPT-5.6-Sol reaches 75.6%.”

Benchmark

Social Gym and SPaRTan

“Our results show that SPaRTan playbooks help GPT-5-mini agents level their performance on weaker roles, but largely do not improve Qwen3-32B's performance.”

Benchmark

SWE-Bench ProMax

“Experiments with frontier models under two agent scaffolds show that the best model achieves only 41.2% resolve rate, confirming that SWE-Bench ProMax presents a meaningful and unsaturated challenge for current AI coding agents.”

Benchmark

Rubric Dropout

“Sweeping the dropout fraction shows a broad 30-50% sweet spot, while the natural alternative, reweighting criteria by how useful they are to training, performs worse than no intervention at all in our setting.”

Scaling Inherently Interpretable Language Models

“Steerling-8B remains competitive with open peer models trained on substantially 2-16x more compute, suggesting a different scaling paradigm: interpretability can be designed into training, and it improves with scale.”

Once Poisoned, Arbitrarily Controlled

“Extensive experiments show that our attack achieves high any-to-any caption-control success rates, preserves clean model utility, and remains effective under several classical backdoor defenses.”

The week by field

Aug 7–13: 163 cleared, 11 frontier

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

Models under test

Papers this week that name the model

  1. GPT-4o mini3
  2. GPT-4o3
  3. DeepSeek-V4-Pro2
  4. gpt-oss-120b2
  5. Qwen3 32B2
  6. Claude Fable 52
  7. GLM-5.22
  8. Claude Opus 52

Labs publishing

Papers this week with an author at the lab

  1. Alibaba4
  2. Tencent4
  3. IBM3
  4. Microsoft3
  5. Anthropic1
  6. Google1
  7. NVIDIA1
  8. OpenAI1

Also cleared

152 papers

All 152 papers