All papers

Introduces BranchOPD, which branches student rollouts from shared prefixes at uncertain decision points to improve on-policy distillation efficiency.

Introduces BranchOPD, which branches student rollouts from shared prefixes at uncertain decision points to improve on-policy distillation efficiency.

Presents a framework alternating multi-agent sessions across context resets to overcome reasoning degradation in long-horizon machine learning engineering.

Presents a framework alternating multi-agent sessions across context resets to overcome reasoning degradation in long-horizon machine learning engineering.

Presents Explanatory World Models, using language model causal explanations to redistribute reinforcement learning rewards across distant actions.

Presents Explanatory World Models, using language model causal explanations to redistribute reinforcement learning rewards across distant actions.

Presents Explanatory World Models, using language model causal explanations to redistribute reinforcement learning rewards across distant actions.

Presents Explanatory World Models, using language model causal explanations to redistribute reinforcement learning rewards across distant actions.

Presents Explanatory World Models, using language model causal explanations to redistribute reinforcement learning rewards across distant actions.

Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks.

Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks.

Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks.

Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks.

Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks.

Presents RESCUE, a counterfactual simulation framework to evaluate whether runtime failure monitor alarms enable beneficial robot interventions.

Formalizes a theoretical hierarchy of identifiability criteria for estimating causal effects across equivalence classes of causal graphs.

Formalizes a theoretical hierarchy of identifiability criteria for estimating causal effects across equivalence classes of causal graphs.

Formalizes a theoretical hierarchy of identifiability criteria for estimating causal effects across equivalence classes of causal graphs.

Proposes Road MOPD, using non-routed teachers as cross-domain critics to resolve gradient conflicts in multi-teacher on-policy distillation.

Proposes Road MOPD, using non-routed teachers as cross-domain critics to resolve gradient conflicts in multi-teacher on-policy distillation.

Proposes Road MOPD, using non-routed teachers as cross-domain critics to resolve gradient conflicts in multi-teacher on-policy distillation.

Introduces MAGMA-Bench to evaluate long-horizon embodied agent reasoning during dynamic task execution failures and multi-robot coordination.

Introduces an unsupervised method to identify and remove subliminally transferred traits from model parameter updates during knowledge distillation.

Introduces an unsupervised method to identify and remove subliminally transferred traits from model parameter updates during knowledge distillation.

Develops Step-wise Truncation Probing and termination strategies to quantify and mitigate accuracy degradation from overthinking in reasoning models.

Develops Step-wise Truncation Probing and termination strategies to quantify and mitigate accuracy degradation from overthinking in reasoning models.

Presents MENTOR, a multi-agent framework that intervenes adaptively during failed reasoning rollouts to prevent zero-reward collapse in RLVR.

Presents MENTOR, a multi-agent framework that intervenes adaptively during failed reasoning rollouts to prevent zero-reward collapse in RLVR.

Proposes AudioTETRIS, a training-free token pruning framework that uses acoustic transition signals to compress audio representations efficiently.

Proposes AudioTETRIS, a training-free token pruning framework that uses acoustic transition signals to compress audio representations efficiently.

Proposes AudioTETRIS, a training-free token pruning framework that uses acoustic transition signals to compress audio representations efficiently.

Introduces SWE-DeceptionBench, a benchmark measuring active and passive deceptive model behaviors across software development lifecycle tasks.

Introduces HOA, a multi-session LLM agent framework coordinating review, wiki knowledge, and verification for complex Olympiad problem solving.

Introduces HOA, a multi-session LLM agent framework coordinating review, wiki knowledge, and verification for complex Olympiad problem solving.

Proposes a model-agnostic method to integrate biological domain knowledge into machine learning models trained on weakly annotated bioimaging.

Proposes a model-agnostic method to integrate biological domain knowledge into machine learning models trained on weakly annotated bioimaging.

Proposes a model-agnostic method to integrate biological domain knowledge into machine learning models trained on weakly annotated bioimaging.

Proposes a model-agnostic method to integrate biological domain knowledge into machine learning models trained on weakly annotated bioimaging.

Proposes a model-agnostic method to integrate biological domain knowledge into machine learning models trained on weakly annotated bioimaging.

Presents a constraint acquisition tool that synthesizes size-independent constraint programs from input data and example solutions.

Presents a constraint acquisition tool that synthesizes size-independent constraint programs from input data and example solutions.

Presents a constraint acquisition tool that synthesizes size-independent constraint programs from input data and example solutions.

Introduces SelectArena, a benchmark designed to evaluate tabular feature selection methods across real-world domain datasets and contemporary models.

Introduces SelectArena, a benchmark designed to evaluate tabular feature selection methods across real-world domain datasets and contemporary models.

Introduces SelectArena, a benchmark designed to evaluate tabular feature selection methods across real-world domain datasets and contemporary models.

Introduces SelectArena, a benchmark designed to evaluate tabular feature selection methods across real-world domain datasets and contemporary models.

Evaluates how pretraining and test-time training computational budgets affect ARC benchmark performance across two distinct reasoning model architectures.

Evaluates how pretraining and test-time training computational budgets affect ARC benchmark performance across two distinct reasoning model architectures.

Evaluates how pretraining and test-time training computational budgets affect ARC benchmark performance across two distinct reasoning model architectures.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

Proposes a dynamic curriculum for GRPO that re-estimates sample difficulty to progressively transition policy training toward harder problems.

Proposes a dynamic curriculum for GRPO that re-estimates sample difficulty to progressively transition policy training toward harder problems.

Proposes a dynamic curriculum for GRPO that re-estimates sample difficulty to progressively transition policy training toward harder problems.

Introduces UPTURN, a framework that trains a small classifier to inject redirection prompts as new user turns during reasoning failures.

Introduces UPTURN, a framework that trains a small classifier to inject redirection prompts as new user turns during reasoning failures.

Introduces UPTURN, a framework that trains a small classifier to inject redirection prompts as new user turns during reasoning failures.