Cover Image

Semantic Deception: When Reasoning Models Can't Compute an Addition

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	de Leeuw, Nathaniël, Nahon, Marceau, Reymond, Mathis, Chatila, Raja, Khamassi, Mehdi
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Strong and weak alignment of large language models with human values
by: Khamassi, Mehdi, et al.
Published: (2024)

Morality is Contextual: Learning Interpretable Moral Contexts from Human Data with Probabilistic Clustering and Large Language Models
by: Morlat, Geoffroy, et al.
Published: (2025)

Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning
by: Chen, Kang, et al.
Published: (2024)

Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
by: Li, Bangzheng, et al.
Published: (2023)

Can't say cant? Measuring and Reasoning of Dark Jargons in Large Language Models
by: Ji, Xu, et al.
Published: (2024)

Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
by: Lee, Heekyung, et al.
Published: (2025)

You Can't Fight in Here! This is BBS!
by: Futrell, Richard, et al.
Published: (2026)

AbsenceBench: Language Models Can't Tell What's Missing
by: Fu, Harvey Yiyun, et al.
Published: (2025)

Scale Can't Overcome Pragmatics: The Impact of Reporting Bias on Vision-Language Reasoning
by: Kamath, Amita, et al.
Published: (2026)

When Models Can't Follow: Testing Instruction Adherence Across 256 LLMs
by: Young, Richard J., et al.
Published: (2025)

Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?
by: Sun, Yiyou, et al.
Published: (2025)

"Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models
by: Halevy, Karina, et al.
Published: (2024)

When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
by: Wang, Kai, et al.
Published: (2025)

Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception
by: Velutharambath, Aswathy, et al.
Published: (2024)

Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
by: Min, Dehai, et al.
Published: (2026)

Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning
by: Dash, Saloni, et al.
Published: (2025)

Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response
by: Liu, Yongkang, et al.
Published: (2023)

Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?
by: Balepur, Nishant, et al.
Published: (2024)

We Can't Understand AI Using our Existing Vocabulary
by: Hewitt, John, et al.
Published: (2025)

Beyond I'm Sorry, I Can't: Dissecting Large Language Model Refusal
by: Prakash, Nirmalendu, et al.
Published: (2025)

D-REX: A Benchmark for Detecting Deceptive Reasoning in Large Language Models
by: Krishna, Satyapriya, et al.
Published: (2025)

LLMs Can't Play Hangman: On the Necessity of a Private Working Memory for Language Agents
by: Baldelli, Davide, et al.
Published: (2026)

When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning
by: Zhu, Rongzhi, et al.
Published: (2025)

AdaptThink: Reasoning Models Can Learn When to Think
by: Zhang, Jiajie, et al.
Published: (2025)

The Point of No Return: Counterfactual Localization of Deceptive Commitment in Language-Model Reasoning
by: Merrill, Scott, et al.
Published: (2026)

SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection
by: Rani, Anku, et al.
Published: (2023)

LLMs Can't Handle Peer Pressure: Crumbling under Multi-Agent Social Interactions
by: Song, Maojia, et al.
Published: (2025)

I Can't Believe It's Not Robust: Catastrophic Collapse of Safety Classifiers under Embedding Drift
by: Sahoo, Subramanyam, et al.
Published: (2026)

When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?
by: Liang, Tuo, et al.
Published: (2025)

Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting
by: Riachi, Roland, et al.
Published: (2025)

An Assessment of Model-On-Model Deception
by: Heitkoetter, Julius, et al.
Published: (2024)

LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench
by: Valmeekam, Karthik, et al.
Published: (2024)

Can LLMs Compute with Reasons?
by: Sandilya, Harshit, et al.
Published: (2024)

If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs
by: Khalifa, Muhammad, et al.
Published: (2024)

Your Teacher Can't Help You Here: Combating Supervision Fidelity Decay in On-Policy Distillation
by: Liu, Yanjiang, et al.
Published: (2026)

Can't See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
by: Wang, Wenxuan, et al.
Published: (2025)

CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
by: Sreedhar, Makesh Narsimhan, et al.
Published: (2024)

When to Reason: Semantic Router for vLLM
by: Wang, Chen, et al.
Published: (2025)

Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment
by: He, Buwei, et al.
Published: (2025)

LH-Deception: Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions
by: Xu, Yang, et al.
Published: (2025)