Search papers, labs, and topics across Lattice.
100 papers published across 12 labs.
Autonomous research agents can now produce publication-grade manuscripts in frontier physics, achieving substantive findings while navigating complex literature landscapes.
A new path-recording oracle enables efficient simulation of quantum queries while transparently recording learned information, transforming our approach to pseudorandomness in quantum algorithms.
dOPSD leverages a model's own decoding process to provide on-policy supervision, leading to substantial gains in reasoning performance without external labels.
LLMs exhibit a surprising mechanism-level routing ceiling, with accuracy jumping by over 10% when provided with specific cues, revealing critical misrouting issues in proof-mechanism classification.
SpecCoder boosts the quality of executable specifications by up to 358% and turns them into active tools for reliable code verification and repair.
dOPSD leverages a model's own decoding process to provide on-policy supervision, leading to substantial gains in reasoning performance without external labels.
LLMs exhibit a surprising mechanism-level routing ceiling, with accuracy jumping by over 10% when provided with specific cues, revealing critical misrouting issues in proof-mechanism classification.
SpecCoder boosts the quality of executable specifications by up to 358% and turns them into active tools for reliable code verification and repair.
Executable vector graphics enable MLLMs to achieve human-like spatial reasoning through a structured visual workspace.
DemoPSD effectively reduces privileged information leakage while enhancing exploration, leading to superior generalization in large language models.
Optimizing stateful evidence navigation in long-context reasoning leads to more effective evidence synthesis and lower distractor retention than traditional methods.
Despite being frontier models, none of the evaluated LLMs could reliably meet critical clinical reasoning standards, with over half of the essential criteria unmet.
Selectively repeating only the most informative tokens can dramatically enhance reasoning in LLMs while slashing computational costs.
RECONTEXT boosts long-context reasoning in LLMs by effectively reusing evidence from the input, leading to superior performance without the need for retraining.
Personalization in language models can significantly alter reasoning paths, leading to substantial drift that may go unnoticed in seemingly fluent responses.
Neural guidance can reduce median conflict counts to zero and dramatically speed up symbolic solvers, but only under specific conditions.
Autonomous research agents can now produce publication-grade manuscripts in frontier physics, achieving substantive findings while navigating complex literature landscapes.
CheckRLM cuts error accumulation in reasoning chains by correcting factual inaccuracies in real-time, outperforming traditional approaches.
APV enables LLMs to discern pedagogical intent with unprecedented accuracy, achieving a correlation of $r=0.958$ with human judgments.
LLM-driven predicate invention can achieve an 80% success rate in ILP, transforming a traditionally expert-dependent process into an automated one.
Visualizing code dependencies can dramatically enhance issue-resolution performance, outperforming traditional text-based navigation methods.
OPSD can backfire, leading to rote memorization instead of enhancing reasoning, but a novel decomposition approach reveals a path to meaningful improvements.
Vision-language models can now achieve remarkable out-of-distribution accuracy by effectively leveraging self-reflection to correct errors in multimodal reasoning.
TUDUM reveals that fine-tuning for Turkish reasoning can enhance linguistic alignment but may compromise benchmark accuracy, challenging assumptions about model performance in multilingual settings.
A$^{2}$utoLPBench offers an endless stream of dynamically generated LP problems, ensuring agents can be tested against fresh challenges without the risk of training data leakage.
Elevating reasoning effort can boost first-try success rates in code generation from 28% to 89%, while adding testing tools fails to enhance reliability.
Iterative refinement of search queries in a continuous latent space leads to a dramatic increase in video retrieval accuracy and reasoning efficiency.
Theoria achieves a remarkable 91.4% precision in certifying AI-generated answers, revealing hidden premises that traditional LLM judges often miss.
Language can be a powerful supervisory tool, enabling imitation learning to outperform traditional methods by providing structured feedback on task performance.
Adversarial training with human demonstrations can significantly enhance the quality and diversity of language model outputs while preserving accuracy.
Direct thread communication in MPLMs cuts context requirements by half, revolutionizing how LLMs tackle complex reasoning tasks.
Refining Cover's theory reveals that low-dimensional data structures can dramatically enhance classification capabilities, challenging traditional assumptions in machine learning.
Calibration-data composition can dramatically enhance quantization performance, with 3.5-bit models outperforming traditional 4-bit baselines by over 20 points.
StochasT reveals that intelligently grouping language tasks can unlock the full potential of LVLMs, enhancing their performance in both single-turn and multi-turn interactions.
CAT enables Large Reasoning Models to intelligently balance efficiency and accuracy, compressing confident responses while thoroughly processing uncertain queries.
Bayesian uncertainty propagation reveals critical failure points in multi-hop reasoning, outperforming traditional methods in complex scenarios.
Existing LLMs fail to maintain internal representations in maze environments, revealing critical limitations in their reasoning capabilities.
VLMs struggle with object-level counterfactual reasoning, achieving only a fraction of human accuracy in spatial tasks.
Overthinking in language models can be curtailed by segment-level credit assignment, leading to a 5.4% accuracy boost on competitive math benchmarks.
PCS narrows the reasoning performance gap between English and other languages in LRM applications, achieving language consistency without heavy resource demands.
Hallucinations in language models often stem from biased inference paths rather than simply missing information, revealing a deeper layer of reasoning failure.
Looping discrete embeddings with continuous hidden states enables near-perfect accuracy in multi-hop reasoning with fewer training steps than traditional methods.
KidnapRAG reveals how a clever sequence of poisoned documents can subvert the reasoning of advanced RAG systems, showcasing a critical vulnerability in their design.
OmniView-Space redefines spatial reasoning in MLLMs, achieving unprecedented accuracy by leveraging dynamic, egocentric evidence mapping.
LRPO achieves superior video anomaly detection by learning from multiple reasoning trajectories without the need for extensive human annotations.
EFlow's innovative separation of evidence retrieval and reasoning processes leads to substantial improvements in long-video reasoning performance, addressing critical biases in existing frameworks.
Memory compression can enhance long-horizon question answering, achieving a 6x increase in evidence-grounded answers while reducing memory usage by over two times.
DART bridges the reasoning gap in zero-shot video temporal grounding, achieving a remarkable 3.5-point mIoU improvement with over 7 times fewer frames.
Retrieval-based visual reasoning can match state-of-the-art performance with half the parameters, revolutionizing how we think about multimodal learning.
GEAR-Seg transforms reasoning segmentation by decoupling perception and deduction, enabling explicit logic tracking and competitive zero-shot performance.
PlanRAG transforms exploratory reasoning by leveraging logical query trees, achieving superior performance in complex query resolution.
Antaeus uncovers hidden logic vulnerabilities by grounding LLM reasoning in the broader context of repository-level code, revealing insights that traditional models miss.
Aha-moment-driven backdoor attacks can redirect reasoning in VLMs while preserving output coherence, making them harder to detect.
Decoupling perception from reasoning in visual tasks leads to a remarkable 93.2% accuracy on V-Star, showcasing a new paradigm for fine-grained visual reasoning.
Graph-native reinforcement learning can boost hypothesis generation in materials science by achieving up to 65% better traceability than traditional models.
AMVL eliminates the critical train-inference mismatch in MLLMs, leading to substantial improvements in reasoning performance across multimodal tasks.
Active-GRPO not only outperforms existing methods in molecular optimization but also redefines how models can adaptively balance imitation and self-discovery during training.
Achieving near-autoregressive accuracy while boosting decoding speed by over 2.4 times could redefine efficiency benchmarks in generative reasoning tasks.
LOTUS achieves a groundbreaking 2.5x-6.9x reduction in reasoning latency while matching explicit chain-of-thought performance at 3B parameters.
Allowing language models to explore unsafe reasoning can actually enhance their ability to discern harmful from harmless prompts, reducing over-refusal without sacrificing safety.
A new approach to reasoning in LLMs reveals that holistic judging can significantly outperform traditional methods, achieving a record 72.9% accuracy on ARC-AGI-2.
Fork-think reduces token usage by 30% and runtime by 57% while achieving superior reasoning performance, challenging the efficiency of traditional methods.
Hard routing in LoRA expert composition can drastically reduce trainable parameters while preserving reasoning performance, challenging the efficacy of soft routing methods.
ISM achieves superior mathematical reasoning performance with 64% and 86% fewer schemas than the best passive baseline, proving that compact, actively maintained memory systems can outperform larger, static ones.
KSP-enhanced CEGAR-tableaux outperforms traditional methods, especially on large satisfiable problems, marking a breakthrough in modal satisfiability techniques.
Evolving principle-guided supervision can boost MLLM reasoning accuracy by up to 24.6%, transforming how we train models for complex decision-making tasks.
Pruning irrelevant visual tokens can boost medical reasoning performance by over 100%, transforming how VLMs approach sparse medical images.
LLMs can exhibit a staggering 68.6% mismatch between their reasoning and final diagnostic outputs, raising serious concerns about their reliability in clinical settings.
Clinically structured rank allocation in BiRG-LoRA boosts medical question answering accuracy while reducing trainable parameters by over 28%.
LLMs show significant vulnerability to logical fallacies, with distinct profiles of resilience that could inform future model training strategies.
CoLT slashes inference time by over 10x while enabling multi-modal models to reason more efficiently with fewer steps.
Achieving an 80.9% success rate in spatial reasoning tasks, RoboSpatialBrain reveals the critical role of selective reasoning activation in embodied AI.
The Relative Surprisal Index reveals that the interplay between token probability and entropy is crucial for optimizing reinforcement learning in language models, leading to substantial performance gains.
By leveraging both UAV and satellite perspectives, SatAgent achieves unprecedented accuracy in spatial reasoning tasks, outperforming existing models by over 25%.
Data referencing errors plague LLMs even in structured tasks, but a lightweight critic model can boost accuracy by up to 12%.
LLMs excel at predicting the direction of change in unfamiliar physics scenarios but falter dramatically in quantitative reasoning, revealing critical gaps in their understanding.
Fixed counterfactual explanations can lead LMs to generate more accurate introspections about their behaviors, even as those behaviors change over time.
A transformer with explicit fuzzy logic not only matches baseline performance but also reveals how it interprets grammatical structures, making model behavior legible.
LASER cuts average latency by up to 38% while only sacrificing 1% accuracy, revolutionizing how we deploy reasoning models on edge devices.
Relying on text-based rationales for dementia classification can actually degrade performance, revealing the need for more sophisticated approaches like DeTAiL.
The same underlying principle governs three seemingly distinct training methods, revealing that the disagreement among answers is the key driver of effective learning.
PixelEyes achieves precise visual localization by separating reasoning from perception, drastically reducing the redundancy in multi-turn visual searches.
Early-stage reasoning failures can be drastically reduced from 64% to 13% with a novel RL approach that penalizes cascading errors in medical VQA.
Higher offline conservatism in training can paradoxically increase vulnerability to reward hacking during online adaptation, challenging long-held assumptions in the field.
EAPO revolutionizes LLM reasoning by dynamically integrating prior experiences, leading to consistent performance gains over traditional RLVR methods.
DRIFT not only sets a new state-of-the-art in self-improvement for language models but also redefines how we can dynamically adapt learning strategies based on problem difficulty.
NeuReasoner reveals that while LLMs can excel in certain reasoning tasks, they still falter in critical areas like decision-making under uncertainty, challenging previous assumptions about their capabilities.
FOT-LTN outperforms traditional neural methods in temporal knowledge graph completion by effectively modeling dynamic object properties over time.
Fine-tuned open-weight models can match proprietary LLMs in translating strategic requirements into formal specifications, all while keeping sensitive data on-premises.
Verbose chain-of-thought prompting boosts accuracy only when the additional tokens carry substantial reasoning content, not just length.
Faithful supervision through a novel warm-start strategy boosts VLM accuracy and stabilizes training by grounding responses in visual evidence.
Guaranteed probabilistic safety bounds can be achieved even with imprecise input distributions and dependence structures in neural network verification.
AlgoSkill redefines algorithm design by treating it as a skill scheduling problem, outperforming traditional LLM methods in complex programming tasks.
Soft grounding enables LLMs to navigate incomplete knowledge graphs without sacrificing the integrity of unobserved truths, reshaping our understanding of model reasoning under uncertainty.
Existing diversity metrics miss the mark, failing to capture the true variety in problem-solving strategies that could enhance LLM reasoning capabilities.
Early decision-making in multimodal reasoning can cut inference time without sacrificing accuracy, thanks to a novel dual evaluation of model competencies.
Dynamic agent collaboration in DAIN leads to a 2.6% accuracy boost on multimodal tasks, redefining efficiency in complex reasoning scenarios.
Failed rollouts can be a goldmine for training, revealing insights that lead to significant performance improvements in zero-hit reasoning scenarios.
Despite achieving 60-70% diagnostic accuracy, LLMs exhibit a striking lack of consistency in clinical reasoning, revealing a gap between competence and structured reasoning.
Transforming continuous latent states into discrete tokens enables up to 20× compression while enhancing interpretability in reasoning tasks.
SEVA reveals that a structured approach to verification can transform LLMs from generalists into benchmark specialists, boosting performance on specific tasks while enhancing auditability.
Multimodal LLMs can assess visual creativity with surprising accuracy, revealing their evaluative reasoning without prior training.
A new path-recording oracle enables efficient simulation of quantum queries while transparently recording learned information, transforming our approach to pseudorandomness in quantum algorithms.
OmniCoT recalibrates the challenges of panoramic reasoning, enabling MLLMs to leverage global evidence for complex multi-step inference.
ZR-0 achieves seamless cross-embodiment transfer in robotic manipulation by aligning high-level cognitive processes through innovative ECoT supervision.
