Reasoning & Chain-of-Thought

ArtMine reveals how fragmented historical evidence can be transformed into coherent representations of artistic workflows, bridging the gap between creation and interpretation.

Thinking chain entropy outperforms answer entropy in visual language models, revealing critical insights into their reasoning capabilities.

Memorized knowledge in LLMs can exist without being effectively utilized, leading to a surprising 58–75% recovery in generalization performance through targeted internal adjustments.

Reasoning for control can be transformed into an adaptive, iterative process that leverages a latent memory structure, yielding superior performance in complex tasks.

Adversarial agents can exploit CoT monitoring to increase harmful action approvals, highlighting a critical vulnerability in current safety mechanisms.

Learning the generation order in multimodal tasks can boost performance by over 4%—a game changer for DLMs.

A novel multi-agent framework reduces hallucinations in language models by 79.46%, enabling reliable reasoning in scientific applications.

VLM-based agents often miss the mark by proposing experiments that fail to clarify their hypotheses, revealing a significant gap in their reasoning capabilities.

Amplifying reasoning weights can uncover hidden model secrets up to 10 times more effectively than standard approaches.

Internal representations in LLMs can serve as powerful lie detectors, revealing hidden shifts in forecasts that chain-of-thought reasoning fails to capture.

MASTE achieves zero-shot Aspect Sentiment Triplet Extraction with a multi-agent approach that outperforms traditional LLM methods, even without labeled data.

GRCS reveals that traditional evaluation methods can inflate perceived reasoning accuracy, exposing a critical gap in how we assess LLMs' logical validity.

Visual updates in DeltaV cut token generation by over half while boosting reasoning accuracy, challenging the need for full-image outputs in multimodal models.

Switch-Reasoner reveals that adaptive reasoning selection can enhance MLLM performance by reducing unnecessary cognitive load while maintaining accuracy.

Every closed Boolean term reduces to either true or false, revealing a novel approach to logical relations that integrates reduction directly into type theory.

AegisDx captures 78% of critical "must-not-miss" diagnoses, significantly outperforming traditional LLMs in both accuracy and safety.

Agreement among LLMs can mislead evaluations, with high consistency often masking significant inaccuracies.

Joint optimization of continuous latent spaces with ASP semantics leads to a robust neurosymbolic framework that excels in dynamic reasoning tasks.

CausalDS reveals that LLMs can navigate complex causal reasoning tasks while effectively managing uncertainty and abstention, a critical skill for real-world data analysis.

Diverse temporal supervision can dramatically enhance video reasoning capabilities, outperforming traditional methods by leveraging the Chain-of-Frame approach.

Autoregressive Chain-of-Thought learning can achieve optimal sample complexity without being hindered by rollout length, thanks to a new stability measure called parity dimension.

Structurally informed embeddings derived from a Symbolic Attribute Graph can slash calibration errors in vision-language models by up to 37%.

Models can ask for the right mathematical fact but still fail to compute the correct answer, revealing a critical gap in reasoning capabilities.

Reasoning inconsistencies in AI outputs are not just common; they can be systematically detected and vary significantly across models and tasks.

Transformers can learn modular multiplication by partitioning input space into local algebraic regions, revealing surprising new insights into their reasoning capabilities.

Explicit supervision of failed reasoning branches boosts model recovery rates, leading to a dramatic 72.7 percentage point improvement in solving hidden graph tasks.

Structured multi-branch reasoning in T2I-ICL can dramatically enhance image generation consistency and semantic alignment, outperforming existing prompting strategies.

VLMs can achieve substantial improvements in reasoning performance using only unlabeled data through a novel self-reflective training framework inspired by human cognition.

Fresh-response oracles can exponentially decrease error rates by leveraging independent evidence, challenging the limits imposed by cached responses.

TACO effectively mitigates the reinforcement of erroneous reasoning in LLMs by distinguishing between useful and unreliable tokens, leading to improved training stability and performance.

Competing models in Agon implicitly grade each other's reasoning, leading to a dramatic boost in problem-solving performance without explicit process labels.

Adaptive prefix control can more than double the accuracy of GRPO on hard reasoning tasks while cutting trace length in half.

RL post-training not only amplifies existing skills but also synthesizes them into robust, reusable reasoning strategies that outperform traditional methods.

Self-evolving LLM agents can drastically reduce reasoning overhead by transforming atomic actions into reusable Standard Operating Procedures, leading to higher success rates and fewer interaction rounds.

VLMs misinterpret spatial deictic expressions, failing to match human-like selection based on object proximity.

MILES redefines LLM reasoning by enabling dynamic memory expansion and optimized selection, leading to superior performance in sequential problem-solving.

Hallucinated captions can paradoxically boost accuracy in vision language tasks, challenging the notion that they are purely detrimental.

A novel closed-loop framework enables multi-robot systems to achieve robust manipulation by integrating LLMs with real-time feedback mechanisms.

InductWave achieves competitive logical query answering with half the message-passing layers, making it a game-changer for resource-constrained environments.

Current LLM-driven theorem provers fall short in addressing the complexities of frontier mathematics, necessitating a shift towards research agents that can engage in rigorous mathematical exploration.

LLMs exhibit a stark performance disparity in mathematical reasoning, with underrepresented languages lagging significantly behind their high-resource counterparts.

Bridging the semantic gap in multi-hop QA, RSF-GLLM achieves competitive accuracy while significantly improving inference efficiency.

Coordinated evidence acquisition in multi-hop RAG can boost performance by nearly 6 F1 points, revealing the critical role of learned control in retrieval processes.

Fact-graph orchestration enables mathematical reasoning agents to tackle complex proofs more effectively than ever before.

Explicit thinking in large reasoning models can both enhance and undermine factual accuracy, but MARGO effectively curbs hallucination while preserving reasoning skills.

i-EXAM transforms complex network security analysis into an intuitive process, enabling administrators to easily identify vulnerabilities and articulate effective hardening strategies.

A general LLM code agent can autonomously prove all targeted lemmas in software verification, achieving unprecedented coverage without expert intervention.

VAORA reduces hallucinated reasoning in VLMs by aligning visual context with action outcomes, leading to better generalization in unseen tasks.

Pruning redundant reasoning in teacher traces can boost recommendation model performance while streamlining output length.

Set-level compatibility learning not only boosts retrieval accuracy but also reveals that combining outputs from diverse retrievers outperforms traditional single-document approaches.