Search papers, labs, and topics across Lattice.
100 papers published across 8 labs.
Over-sampling can lead language models to confidently select incorrect answers, revealing a critical limit to test-time scaling that researchers must heed.
Combining reasoning-guided and gradient-guided prompt optimization leads to significant performance improvements in few-shot relation extraction tasks.
MoD achieves 3.7x lower latency and 87% less token consumption while outperforming traditional multi-agent debate systems in accuracy.
Models that write intermediate states significantly outperform those that only report final answers, achieving up to 91% accuracy in predicting outcomes from edited states.
HIPPO reveals that hint-injected pairwise aggregation can effectively eliminate shortcut reasoning in LLMs, leading to more authentic and transferable reasoning skills.
Combining reasoning-guided and gradient-guided prompt optimization leads to significant performance improvements in few-shot relation extraction tasks.
MoD achieves 3.7x lower latency and 87% less token consumption while outperforming traditional multi-agent debate systems in accuracy.
Models that write intermediate states significantly outperform those that only report final answers, achieving up to 91% accuracy in predicting outcomes from edited states.
HIPPO reveals that hint-injected pairwise aggregation can effectively eliminate shortcut reasoning in LLMs, leading to more authentic and transferable reasoning skills.
EntroRouter achieves near-optimal expert accuracy while slashing computational costs by nearly half, challenging the status quo of model routing efficiency.
Achieving over 134% accuracy gains with just 1,000 trainable parameters reveals a game-changing approach to enhancing spatial reasoning in vision language models.
Evaluating LLM agents in microservice failure diagnosis reveals that traditional outcome-based benchmarks miss critical reasoning processes, which these new datasets effectively capture.
Evolved agents can learn to dynamically coordinate multiple retrieval strategies, leading to a remarkable 19.6-point performance boost in multimodal document reasoning.
EFT enables LLMs to evolve solutions across diverse optimization tasks, achieving over 10% performance gains and state-of-the-art results in challenging mathematical problems.
Over-sampling can lead language models to confidently select incorrect answers, revealing a critical limit to test-time scaling that researchers must heed.
ProMSA achieves superior accuracy in KB-VQA by dynamically selecting retrieval strategies, outperforming traditional fixed pipelines.
Cognitive episodes in LRM reasoning traces reveal that item difficulty is shaped more by problem-solving dynamics than by item text alone.
MLLMs struggle with video temporal-logical reasoning, showing a substantial performance gap compared to human capabilities, especially as complexity increases.
Amplifying time-awareness in LLMs can drastically reduce look-ahead bias, enhancing their forecasting accuracy without sacrificing general reasoning skills.
RolloutPipe slashes rollout-to-training time by up to 42% while keeping on-policy correctness intact, revolutionizing resource efficiency in LLM training.
E-TTS achieves up to a 33.14% performance boost in robotic manipulation by leveraging historical context and iterative refinement, redefining how we approach test-time scaling.
Metacognitive regulation emerges as a key differentiator for deeper collaboration in human-AI interactions, reshaping our understanding of dialogue dynamics.
Visualizing counterfactuals can unlock reasoning capabilities in LLMs that text alone cannot achieve.
CoT training may not boost reasoning capabilities as expected, instead reinforcing reliance on prompts, which could reshape our approach to training language-model agents.
Hallucination rates drop to just 8.1% with TAVR-VLM, revolutionizing the reliability of AI-generated surgical reports.
ReasonCLIP-58M shows that structured reasoning supervision can dramatically boost CLIP's reasoning abilities while maintaining efficiency.
LeanGuard achieves 82.90 F1 score with 100x less compute than traditional reasoning guards, challenging the need for complex reasoning in safety moderation.
Retaining the original question in multilingual reasoning cascades can dramatically enhance performance, revealing that context is key to effective translation and reasoning.
Traditional measures of sentence processing fail to account for garden path difficulties, but a new framework reveals that syntactic belief updates are key to understanding these challenges.
Role-playing agents can achieve human-like character fidelity by integrating psychology-grounded reasoning with advanced reinforcement learning techniques.
Aligning soft-token representations across languages can boost multilingual reasoning accuracy by over 17 points, especially for low-resource languages.
KIRP achieves state-of-the-art stance detection in Japanese tweets by effectively integrating external knowledge with innovative reasoning techniques.
LRMs allocate more resources to problems they get wrong, while humans engage more with problems they expect to solve, revealing a fundamental difference in reasoning strategies.
EpiKV achieves 72% accuracy on MATH-500 with a 4096-token cache, rivaling the best attention-based methods while dramatically improving inference speed.
A versatile framework for hybrid modal logic that simplifies the specification and verification of programming languages and security protocols without additional syntactic overhead.
LLMs generate functional specifications with over 91% accuracy, but struggle with verification success, revealing a critical gap in domain knowledge for separation logic.
CORTEX reveals that structured reasoning in 3D chest CT MLLMs can enhance diagnostic transparency and reliability, bridging a significant gap in medical imaging AI.
Visual under-conditioning in LMMs can be overcome by directly regularizing visual attention, leading to remarkable improvements in multimodal understanding tasks.
Proprietary models may have powerful reasoning engines, but they fail to accurately estimate metrics and leverage structural insights, revealing a crucial gap in VLM performance.
Rebinding visual cache positions can boost multimodal reasoning accuracy by 5% while slashing computational costs dramatically.
Tokens with high predictive uncertainty can dramatically enhance long-context reasoning, outperforming traditional attention-based methods in KV cache compression.
LLMs struggle to connect identified root causes to their causal paths, achieving only 61.5% success in grounding diagnoses despite a 76% identification rate.
Identifying challenging reasoning examples using just the first 100 tokens can drastically reduce data curation costs while enhancing model performance.
Achieving unprecedented accuracy in Open Relation Extraction, ReaORE outperforms traditional methods by effectively distinguishing between easily confused relations through advanced reasoning techniques.
LLMs excel at factual recall but falter on quantitative reasoning and conceptual tasks, revealing critical gaps in their domain-specific capabilities.
NebulaExp reveals that a meticulously curated dataset and innovative reinforcement learning strategies can boost LLM performance significantly, achieving up to 4.43 points improvement in instruction-following tasks with minimal data.
LLMs excel at traditional riddles but falter dramatically when faced with riddle riddles, revealing a critical limitation in their reasoning flexibility.
Distinguishing negative samples can boost LLM reasoning performance on ARC-like tasks by providing critical near-miss alternatives.
Merging logical structures with linguistic context allows for a dramatic leap in fallacy classification accuracy, outperforming traditional methods.
Unsupervised training can elevate multimodal models, achieving a 3.5% boost in understanding metrics and a notable increase in image generation fidelity without human intervention.
Despite high fluency scores, large language models struggle with procedural reasoning in investment contexts, exposing a critical gap in their capabilities.
MiniOpt achieves the highest solving accuracy for compact models while requiring significantly fewer training resources than traditional methods.
SR-PPO achieves significant gains in reasoning tasks by effectively assigning credit to individual tokens from a single rollout, transforming how we approach reinforcement learning in language models.
Low-bit quantization can inflate reasoning length, leading to hidden compute costs that traditional accuracy metrics overlook.
FactorLibrary enables a 91.8% success rate in finding optimal polynomial circuits, revolutionizing how we tackle combinatorial complexity in algebra.
No finite axiomatization exists for measurable social decision frames, revealing deep limitations in majority reasoning frameworks.
Position graphs reveal that even a constrained graph framework can harbor NP-complete challenges in structural pattern discovery.
Identifying cliff tokens reveals that a single token can drastically shift LLM performance, with targeted removal leading to near-perfect accuracy in mathematical reasoning tasks.
Bridging the gap between informal and formal mathematics, TheoremGraph reveals 18.3 million dependencies that can enhance mathematical search and reasoning.
Riazi-8B outperforms existing Urdu models in mathematical reasoning, proving that targeted language adaptation can bridge the gap in low-resource AI applications.
OPERA's intrinsic reward mechanism enables LLMs to achieve new heights in open-ended reasoning, outperforming proprietary models without the pitfalls of biased supervision.
Local Branch Routing enables language models to leverage contextual evidence for decision-making without the computational burden of full solution searches, leading to substantial improvements in reasoning accuracy.
OracleAnalyser achieves superior analytical performance on oracle bone scripts with just 3 billion parameters, outperforming larger models.
Transforming long-form videos into compact, temporally grounded scene graphs allows MLLMs to maintain semantic richness while overcoming input constraints, leading to state-of-the-art VQA performance.
Comparison-based supervision can drastically enhance reasoning quality in medical AI, outperforming traditional reward systems.
SAGE-Nav achieves state-of-the-art navigation efficiency and zero-shot generalization by seamlessly integrating LLM planning with dynamic scene graphs.
Heterogeneous LLM collaboration boosts robotic task planning success rates by ensuring plans are both feasible and efficient.
Falcon redefines threat assessment in X-ray screening by shifting from object detection to relational reasoning about component interactions.
MLLMs can significantly reduce hallucinations and improve reasoning by shifting focus from linguistic shortcuts to causal visual grounding through VIGIL.
Narration-of-thought transforms ethical reasoning in LLMs, slashing stakeholder collapse and uncertainty suppression to near-zero levels.
ASTs and PDGs outperform raw source code in LLM vulnerability reasoning, revealing that more context isn't always better.
V-Zero achieves fine-grained visual reasoning without any annotated answer labels, outperforming traditional methods in both speed and accuracy.
Correct reasoning in LLMs can be viewed as navigating deep attractor basins, leading to a 5.38% performance boost on GSM8K when leveraging Gibbs-weighted retrieval.
The first practical exact algorithm for cost-sensitive evaluation of stochastic Boolean functions reveals a surprising efficiency-quality trade-off that could transform decision-making processes in complex scenarios.
The latest definition of actual causation collapses three competing theories into one, exposing a critical flaw in the entire framework of causal analysis.
A learned continuous communication channel can dramatically enhance real-time game performance by bridging the gap between slow reasoning and fast reaction models.
RaDaR can identify rare diseases 1.87 months earlier than traditional methods, revolutionizing diagnostic timelines for patients.
Minimally complete abstractions can be expressed in OBDA without increasing complexity, revealing new insights into query processing.
A novel approach that boosts LTV prediction for billions of low-activity users by transforming sparse profiles into actionable insights without heavy LLM reliance.
ReM-MoA reveals that structured cross-layer reasoning memory can dramatically enhance the scalability of multi-agent systems, outperforming traditional methods as complexity increases.
Nuclear strategies prevail in LLM warfare, revealing that belief-tracking errors, not cognitive failures, drive illegal actions in adversarial settings.
Efficient reasoning under Rational Closure for DL-Lite can be achieved with minimal overhead, transforming how defeasible knowledge is handled in AI applications.
Confidence calibration in reasoning models can be dramatically improved by aligning supervision with the model's state, cutting ECE by over half on challenging benchmarks.
Bridging the gap between informal mathematics and formal proofs, this method enables LLMs to auto-formalize complex mathematical reasoning while preserving its inherent ambiguity until the final verification stage.
PointVG-R achieves a groundbreaking 15.86-point boost in mIoU by integrating geometric reasoning into visual grounding tasks, reshaping our approach to spatial interpretation in models.
Structured scene representations can dramatically enhance an agent's ability to reason and plan over long horizons, outperforming traditional methods in real-world scenarios.
LALMs can achieve superior emotional comparison accuracy with only 5% of the training data typically required by conventional methods.
SER's innovative approach to grounding video reasoning demonstrates a 3.0-point leap in accuracy by integrating semantic verification into the reward structure.
IV-CoT reveals that separating structural planning from appearance rendering can dramatically enhance the fidelity of text-to-image generation.
Even the top-performing language models struggle with archive-grounded reasoning, achieving only 59.4% accuracy on a benchmark designed to test their agentic capabilities across diverse workplace documents.
T2I models falter dramatically in counterfactual scenarios, revealing their dependence on familiar visual patterns rather than true causal reasoning.
Helpfulness in LLMs can dangerously suppress critical causal reasoning, with Causal Caution plummeting to just 0.5% in practical contexts.
Prioritizing domains based on their cross-domain transferability can boost multi-domain RLVR performance by up to 10%.
Dense rewards can transform how we approach multi-view reasoning, leading to substantial performance gains in 3D visual question answering.
Optimizing the order of thought in diffusion models can boost accuracy by over 9% on complex tasks like Sudoku and mathematical reasoning.
SQLConductor achieves a remarkable 73.2% execution accuracy by dynamically adapting SQL orchestration based on real-time feedback and intermediate results.
Training LLMs on incorrect outputs can yield better reasoning performance than focusing solely on correct ones, challenging conventional wisdom in model distillation.
LLM-generated waypoints can halve the search space in non-geometric graphs without sacrificing optimality.
Adaptive interleaved reasoning boosts MLLMs' numerical computation accuracy by nearly 10 percentage points, revolutionizing their tool-use capabilities.
Abandoning arithmetic logic for string similarity allows LLMs to achieve unprecedented accuracy in deducing logical rules from binary strings.
SPIRAL achieves a remarkable 15% performance increase by combining sequential, parallel, and aggregative reasoning in language models.
Clinical agents face a staggering 62.3% accuracy ceiling in complex EHR reasoning, revealing significant gaps in current AI capabilities.
ReasoningLens turns the opaque reasoning of large models into clear, actionable insights, enabling researchers to pinpoint errors and optimize performance like never before.
DART boosts reasoning accuracy by up to 22.5 points while slashing thinking token usage by over 50%, all without requiring labeled training data.
Current LLMs achieve only 50.64% accuracy on a new benchmark for higher-order logical reasoning, revealing a critical gap in their reasoning capabilities.
