👥 Social Computing

🧪 ICML2025 · 6 paper notes

📌 Same area in other venues: 📷 CVPR2026 (3) · 🔬 ICLR2026 (17) · 💬 ACL2026 (45) · 🧪 ICML2026 (9) · 🤖 AAAI2026 (10) · 🧠 NeurIPS2025 (20)

🔥 Top topics: LLM ×2

DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts

DEFAME is proposed, which is a modular, zero-shot multimodal LLM pipeline. By using a six-stage dynamic workflow (Plan -> Execute -> Summarize -> Develop -> Predict -> Justify) combined with external multimodal tool retrieval for evidence, it achieves end-to-end joint text-image fact-checking, reaching new SOTA performance on three benchmarks: AVeriTeC, MOCHEG, and VERITE.

Dynamical Phases of Short-Term Memory Mechanisms in RNNs

This work discovers two distinct underlying dynamical mechanisms supporting short-term memory in RNNs—slow-point manifolds and limit cycles. It analytically derives the power-law scaling laws of their maximum learnable learning rates using toy models (SP: \(\beta\) approx. 4-5 vs LC: \(\beta\) approx. 2-3), and provides large-scale empirical validation by training approximately 80,000 RNNs.

Learning Survival Distributions with the Asymmetric Laplace Distribution

This paper proposes a parametric survival analysis method based on the asymmetric Laplace distribution (ALD). By using a neural network to learn the three parameters of the ALD (location, scale, and asymmetry), it achieves continuous, closed-form estimation of the survival distribution, comprehensively outperforming existing parametric and non-parametric approaches in both discriminative and calibration performance.

OR-Bench: An Over-Refusal Benchmark for Large Language Models

This work proposes OR-Bench, the first large-scale over-refusal benchmark for LLMs. It contains 80K safe prompts that are prone to being falsely refused, revealing a strong trade-off between safety and over-refusal with a Spearman correlation coefficient of up to 0.89.

Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing

This paper proposes the GETA framework, which integrates Computerized Adaptive Testing (CAT) from psychometrics with Automatic Item Generation (AIG). Utilizing a variational IRT model and an LLM-driven item generator, GETA dynamically probes the value boundaries of LLMs to address the "evaluation chronoeffect" (data leakage and difficulty saturation) inherent in static benchmarks.

When Bad Data Leads to Good Models

This paper proposes a "pre-training/post-training co-design" perspective, demonstrating through controlled experiments that incorporating a moderate amount of toxic data (~10%) into pre-training data actually reduces the entanglement of toxic features. This makes the model easier to detoxify during post-training (e.g., via ITI activation steering), ultimately reducing toxicity on ToxiGen from 41.40 to 2.63 while maintaining language capabilities.