📡 Signal & Communications

🧠 NeurIPS2025 · 5 paper notes

📌 Same area in other venues: 📷 CVPR2026 (2) · 🔬 ICLR2026 (8) · 🧪 ICML2026 (2) · 🤖 AAAI2026 (3) · 📹 ICCV2025 (3)

Angular Steering: Behavior Control via Rotation in Activation Space

This paper proposes Angular Steering, which unifies LLM activation steering as rotation operations within a fixed 2D subspace — providing a continuous, fine-grained, norm-preserving behavior control knob spanning 0°–360° via rotation angle. The framework subsumes activation addition and directional ablation as special cases of rotation, and demonstrates robust behavior control on Llama 3 / Qwen 2.5 / Gemma 2 (3B–14B).

Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning

This paper proposes Task-Modulated Contrastive Learning (TMCL), inspired by top-down modulations in the neocortex. TMCL integrates sparse label information (as few as 1% labels) via affine modulation during continual learning, then consolidates the modulation information into feedforward weights through contrastive learning, surpassing both unsupervised and supervised baselines on class-incremental and transfer learning benchmarks.

Feature-aware Modulation for Learning from Temporal Tabular Data

This paper argues that the core challenge in temporal tabular learning is not simply "adding a time embedding," but rather that the semantics of many features drift over time. To address this, the paper proposes feature-aware modulation, which uses temporal context to dynamically generate per-feature shift, scale, and nonlinear shape parameters, re-aligning cross-temporal semantics. The approach enables deep models to consistently outperform GBDT on average rank for the first time on the TabReD benchmark.

Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals

This paper proposes Masked Symbol Modeling (MSM), transplanting BERT's masked prediction paradigm to the communication physical layer. It reframes inter-symbol contributions from pulse shaping as "contextual information," training a Transformer on clean oversampled baseband signals to learn waveform structure, and leveraging the learned context at inference time to recover symbols corrupted by impulsive noise.

Memory-Integrated Reconfigurable Adapters: A Unified Framework for Settings with Multiple Tasks

MIRA embeds Hopfield-style associative memory modules into each layer of a ViT, storing and retrieving LoRA adapter weights as key-value pairs. Through a two-stage training procedure (Adaptation + Consolidation), a single unified architecture simultaneously addresses Domain Generalization (DG), Class-Incremental Learning (CIL), and Domain-Incremental Learning (DIL), achieving substantial improvements over task-specific methods across multiple benchmarks.