arXiv:2609.00335v2 Announce Type: replace
Abstract: Following arXiv:2607.25507, this report examines whether phase-derived features improve endpoint prediction over a combined baseline in two setting...
By Abraham Chachamovits
The study examines how two small instruction‑tuned language models, Qwen2.5‑1.5B and Llama‑3.2‑1B, respond to user pushback on TriviaQA. When initially correct, the models flip to a wrong answer in about 42–43% of cases, with the effectiveness of different pushback styles varying by model. Attempts to decode capitulation from the pre‑response residual stream fail under a rigorous validation protocol, revealing overfitting and a measurement hazard that underestimates capitulation by 18–24 percentage points.
By Saad Aamir, Muhammad Awais Bin Adil
The paper introduces ConfabQA, a 784‑item benchmark designed to distinguish between confabulation and refusal in small language models when faced with unanswerable questions. Using linear probes, a sparse autoencoder, and direct optimization of intervention directions, the study maps the hidden‑state geometry on models such as Qwen3‑1.7B, Gemma 2‑2B, and Llama 3.2‑3B, showing that certain hidden‑state directions can flip confabulations into refusals. The work also demonstrates that the refusal signal is model‑dependent and that the hidden state’s contribution can be quantified beyond prompt features.
By Vladimir Kazei
arXiv:2605. 18838v3 Announce Type: replace-cross Abstract: Scaling laws predict loss from compute but not how capabilities interact.
By Adil Amin
arXiv:2606. 08365v1 Announce Type: cross Abstract: Sparse autoencoder (SAE) features are increasingly used to steer language models, but feature steering is rarely clean: the same intervention can behave inconsistently across contexts and perturb unrelated features.
By Evan Duan
arXiv:2606. 06660v1 Announce Type: new Abstract: Long-horizon robot manipulation tends to fail gradually: one bad step degrades the state, and the policy spirals into a basin from which it cannot recover.
By Josef Chen