Claims Browser

Clinical language models exploit note-specific artifacts (templates, separators, boilerplate) that do not reflect patient state, causing them to fail under deployment shifts despite strong in-hospital accuracy

"Clinical language models can achieve strong in-hospital accuracy yet fail under deployment shifts because they exploit note-specific artifacts (e.g., templates, separators, boilerplate) that do not reflect patient state."

CAST (Concept-guided Artifact Suppression Tuning) uses Sparse Autoencoders to expose sparse, human-auditable features from intermediate Transformer activations for clinical text classification

"CAST uses Sparse Autoencoders to expose sparse, human-auditable features from intermediate Transformer activations"

CAST improves over fine-tuned encoder baselines and remains competitive with strong LLM baselines on MIMIC-IV discharge-note mortality prediction

"On MIMIC-IV discharge-note mortality prediction, CAST improves over its corresponding fine-tuned encoder baselines and remains competitive with strong LLM baselines"

SAE-based approaches can provide auditable, feature-level audit trails showing clinical concepts supporting predictions and artifact concepts suppressed during training

"while producing a feature-level audit trail of the clinical concepts that support each prediction and the artifact concepts suppressed during training"

Large language models organize moral knowledge geometrically, with moral foundation representations spanning near-maximal independent dimensions while sharing a positive common component

"We find the directions neither collapse into a single moral detector nor isolate from one another. Rather, they span a near-maximal number of independent dimensions while sharing a positive common component."

The shared component in moral representation is moral-specific with much higher integration compared to matched non-moral concept batteries

"The shared component is the signature of integration, and it is moral-specific relative to a matched non-moral concept battery built identically (mean pairwise cosine 0.26 vs. 0.013)."

Moral knowledge geometry in LLMs is consistent across architectures and scale, and emerges early in pre-training before probe accuracy saturates

"The geometry is consistent across architectures and scale and reaches its integration regime early in pre-training, well before probe accuracy saturates."

LLM moral representations reflect corpus statistics rather than the individualizing/binding distinction predicted by Moral Foundations Theory

"The structure the model discovers shows no evidence of the individualizing/binding distinction predicted by Moral Foundations Theory (an underpowered test: only 20 candidate partitions exist) but rather reflects corpus statistics."

LLMs represent moral tension itself in dilemmas rather than pre-resolved judgments, with dilemma directions partially composing from component foundations but majority variance encoding conflict-specific structure

"Extending to moral dilemmas, each dilemma direction partially composes from its component foundations, at 2.7x a mismatched-pair baseline, while the majority of its variance encodes conflict-specific structure. The model represents moral tension itself, not a pre-resolved judgment."

State-of-the-art action-conditioned video models are restricted to single robot embodiments and cannot leverage heterogeneous video data

"State-of-the-art action-conditioned video models are typically restricted to a single robot embodiment, preventing them from leveraging the vast corpus of heterogeneous video data that contains rich signals for learning generalizable physics."

CLAP is capable of being trained on diverse, internet-scale videos across human and robotic agents for cross-embodiment action-conditioned video generation

"we introduce CLAP, a framework for cross-embodiment action-conditioned video generation capable of being trained on diverse, internet-scale videos across human and robotic agents."

CLAP approaches or surpasses state-of-the-art single-embodiment video models in challenging environments like DROID

"Crucially, CLAP approaches or surpasses state-of-the-art single-embodiment video models in challenging environments like DROID."

CLAP delivers the most comprehensive suite of action-conditioned video world models to date spanning diverse action-conditioning spaces and robot morphologies

"CLAP delivers the most comprehensive suite of action-conditioned video world models to date - spanning diverse action-conditioning spaces (end-effector, language, and latent) and robot morphologies (including cross-embodiment, DROID, Bridge, bimanual YAM robots, and G1 humanoids)."

Large Reconstruction Models (LRMs) provide a powerful geometric scaffold that preserves relative human-object arrangement and proximity cues for 3D human-object interaction reconstruction

"Our key observation is that LRMs provide a powerful geometric scaffold that preserves relative human-object arrangement and proximity cues."

MILO achieves strong reconstruction accuracy and outperforms existing baselines across multiple benchmarks and interaction scenarios for 3D human-object interaction estimation

"MILO achieves strong reconstruction accuracy and outperforms existing baselines across multiple benchmarks and interaction scenarios."

Using LRMs significantly simplifies the 3D human-object interaction reconstruction procedure by reframing it as interpreting the LRM mesh rather than fitting models to 2D images

"This significantly simplifies the reconstruction procedure, reframing the problem as interpreting the LRM mesh: we segment it into human and object components, fit a parametric body model to the human part, and optionally align an object template to the object part"

Reinforcement learning with verifiable rewards (RLVR) improves specific capabilities of large language models

"Reinforcement learning with verifiable rewards (RLVR) improves specific capabilities of large language models"

The average performance difference between Merge, Mix RL, and MOPD fusion paradigms is at most 1.4 points, but can reach 8.6 points on individual benchmarks

"Although their average performance differs by at most 1.4 points, the gap reaches 8.6 points on a single benchmark"

All three fusion paradigms improve single-sample accuracy without measurable gains in solution coverage or losses in held-out capabilities

"All three improve single-sample accuracy without measurable gains in solution coverage or losses in held-out capabilities"