September 29 – October 5: 18 frontier papers out of 4,166 read.
Papers rated as moving the field
How we choose“Instead of utilising true path integration or forming geometric survey knowledge, we find that VLMs rely almost entirely on 2D visual recognition and text-matching to bypass complex spatial reasoning.”
The authors, in the abstract
A New Frontier for Large-Scale Spatial Intelligence in VLMs
“We compare the performance of a cascading system to open-weight and closed SLMs on MULTISPEECH-BENCH and find that the cascading system outperforms open-weight SLMs but not all closed SLMs.”
“Our extensive experiments demonstrate that RMB significantly improves reward accuracy on both in-distribution and out-of-distribution datasets, substantially mitigating the reward hacking issue and ultimately improving RLHF performance.”
“Applying our methods to GPT-6 Astra and previous OpenAI models, with cyber safeguards disabled, we find that GPT-6 Astra attempts complete supply-chain attacks in simulation at a higher rate than GPT-5.6 Sol and GPT-5.5.”
“Evaluating 13 frontier models, we find substantial variation in end-to-end repository generation ability, with pass@1 ranging from 11.7% to 67.7%, providing strong model differentiation while leaving considerable headroom for future progress.”
“Evaluation of seven frontier models reveals a substantial capability gap: the strongest model achieves an EngiScore of only 44.3, and just 3.6% of multi-software attempts succeed.”
“Experiments across multiple Code-as-Policy embodied tasks show that RE-0 improves both teacher-assisted execution and the standalone student, and generalizes to novel robots and scenes.”
“In AgentDojo, it reduces non-DoS targeted attack success from 20.82% to 0.40%.”
“Our results show that current state-of-the-art VLMs with a strong harness still face challenges in addressing key questions in the scientific domains.”
“Experiments show that SARA significantly mitigates deceptive safety alignment under both standard and adversarial settings while preserving helpfulness and utility.”
“Through extensive experiments on mathematical reasoning and code generation tasks, we demonstrate that our method significantly expands the accuracy-fidelity Pareto front.”
“We find that model pairs can learn to communicate the secret using only one bit of feedback indicating whether the receiver inferred it correctly.”
“We show that simple attacks can steal reasoning capabilities from existing closed-source language models using data easily obtainable from current APIs, yielding reasoning improvements equivalent to more sophisticated attacks that extract the full hidden traces.”
Sep 29 – Oct 5: 387 cleared, 18 frontier
Papers this week that name the model
Papers this week with an author at the lab
369 papers, by field