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Alex Ferrando de las Morenas

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2

RESEARCH · 1 SOURCE · Apple Machine Learning Research

The Communication Bottleneck: round-trip study of tree-structured expression serialization in language models

The paper proposes a round-trip protocol to measure how well tree-structured arithmetic expressions survive serialization into natural-language word problems and back. Evaluating 16 models pairwise, the authors find the channel is lossy and asymmetric (swapping generator and extractor can change accuracy by up to 60.4 points), the best generator–extractor pair achieves 92.9% round-trip correctness, at least 73.6% of failures originate in generation, structure (operator count, depth, right-branching) drives difficulty more than model family, and ~3,600 targeted fine-tuning examples substantially improve open-weight models (raising them above untrained Gemini-3.1-Pro and helping in disjoint-domain cases).

7.0

RESEARCH · 1 SOURCE · Apple Machine Learning Research

Dynamically Scaled Activation Steering (DSAS) adaptively modulates steering in generative models

DSAS is a method-agnostic framework that decouples when to steer from how to steer by computing context-dependent scaling factors to modulate the strength of existing activation-steering transformations across layers and inputs. The authors report that DSAS improves the trade-off between toxicity mitigation and utility preservation, adds minimal compute overhead, improves interpretability by highlighting tokens that require steering, and can be jointly optimized with steering functions; the paper was accepted to the UniReps workshop at NeurIPS 2025 and the authors say code will be made available on GitHub.

6.0