COLM 2025PastMath & reasoningLarge language modelsInterpretability
The First Workshop on the Application of LLM Explainability to Reasoning and Planning
XLLM-Reason-Plan
- Submission deadline
- Jun 28, 2025, 23:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (19)
Fetched from OpenReview (v2) on 2026-06-11.
Angular Steering: Behavior Control via Rotation in Activation Space
Are General-Purpose LLMs Ready for Planning? A Large- Scale Evaluation in PDDL
Attributing Response to Context: A Jensen–Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation
Before You 〈think/〉, Monitor: Implementing Flavell's Metacognitive Framework in LLMs
Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation
Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction
Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsing
Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones
From Indirect Object Identification to Syllogisms: Exploring Binary Mechanisms in Transformer Circuits
How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence
HYBRIDMIND: Meta Selection of Natural Language and Symbolic Language for Enhanced LLM Reasoning
Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer
Reasoning Riddles: How Explainability Reveals Cognitive Limits in Vision-Language Models
ReCalibrate: RL for Uncertainty-Aware Reasoning in LLMs
Rethinking (Human) Preference Evaluation of LLM Rationales
The Geometry of Self-Verification in a Task-Specific Reasoning Model
When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration