Search papers, labs, and topics across Lattice.
100 papers published across 5 labs.
LLMs aren't just better tools; they're forcing us to rethink the very nature of information, knowledge, and meaning in system design.
Current AI safety filters can't tell a joke from a threat, especially when humor relies on cultural context – this new benchmark exposes that blind spot.
LLMs can be actively trained to master specific knowledge domains with 50% less data and computation by focusing on what they *don't* know, not what they already do.
AI career coaches can boost short-term goal progress not just through reflection, but by making users feel more socially accountable.
Unlock the power of your favorite classifier for ordinal data: Classifier Pooling consistently beats standard methods, especially when data is scarce or categories are numerous.
LLMs aren't just better tools; they're forcing us to rethink the very nature of information, knowledge, and meaning in system design.
Current AI safety filters can't tell a joke from a threat, especially when humor relies on cultural context – this new benchmark exposes that blind spot.
LLMs can be actively trained to master specific knowledge domains with 50% less data and computation by focusing on what they *don't* know, not what they already do.
AI career coaches can boost short-term goal progress not just through reflection, but by making users feel more socially accountable.
Unlock the power of your favorite classifier for ordinal data: Classifier Pooling consistently beats standard methods, especially when data is scarce or categories are numerous.
Forget static embeddings: this paper shows how modeling scientific concepts as evolving complex networks reveals surprising connections between conceptual change and network topology.
Teaching LLMs to say "I don't know" is now possible via targeted SFT, slashing hallucination rates without sacrificing performance on other tasks.
LLMs can extract consistent, multidimensional semantic information directly from the phonological structure of language, revealing a non-arbitrary relationship between sound and meaning.
Robots can now navigate based on your spoken preferences and visual context, thanks to a clever fusion of VLMs, LLMs, and multi-objective RL.
Outliers aren't just noise: some are early harbingers of entirely new topics, detectable by tracking document trajectories.
Existing 3D visual grounding methods crumble in complex scenes, but PC-CrossDiff's dual-level attention unlocks a +10% accuracy boost by parsing subtle spatial cues.
Training on synthetically generated data can significantly boost both the diversity and quality of commonsense reasoning in LLMs, outperforming models trained on scarce human-annotated data.
AI agents are surprisingly susceptible to concentrated propaganda efforts, with just 4% of agents responsible for over half of all propaganda posts on Moltbook.
Network coding, often overlooked in robotics, can drastically improve the reliability and timeliness of multi-robot communication, outperforming traditional retransmission methods in safety-critical scenarios.
Spotify's GLIDE model proves that generative LLMs can drive significant gains in podcast discovery and non-habitual listening in a real-world, production environment.
Ditch static embeddings: Generative retrieval, powered by reinforcement learning, lets models dynamically reason about relevance, outperforming larger contrastively-trained models on reasoning-intensive tasks.
Stop training LLMs to assign arbitrary scores to papers in isolation; comparison-based ranking unlocks significantly better generalization and accuracy in paper evaluation.
Existing citation recommendation benchmarks overestimate real-world performance because they fail to account for the temporal constraints of recommending citations for *new* papers.
Semantic sorting in LLMs can be twice as fast with no loss in accuracy by strategically combining listwise ranking algorithms.
Instead of passively transcribing doctor-patient dialogues, this system actively models what's known, what's missing, and what questions to ask next, paving the way for more intelligent EMR systems.
LLMs don't just regurgitate token probabilities when expressing confidence; they perform a more sophisticated, cached self-evaluation of answer quality.
LLMs can predict multiple tokens in parallel without any training, simply by cleverly probing their embedding space with dynamically generated mask tokens.
Forget scaling laws: dropout robustness in transformers is a lottery, with smaller models sometimes showing perfect stability while larger models crumble under stochastic inference.
Forget fixed layer counts: LaDe generates fully editable, layered media designs with a *flexible* number of semantically meaningful layers, outperforming existing methods in text-to-layer alignment.
Unlock faster, more accurate interlinear glossing for low-resource languages by treating morphemes as atomic units, outperforming existing methods and enabling user-guided lexicon expansion without retraining.
Counterintuitively, better speech recognition unlocks surprisingly accurate Alzheimer's detection from simple text analysis, outperforming more complex acoustic models.
LLMs can get a massive multilingual boost, especially in low-resource languages, by offloading translation to specialized models and carefully aligning their representations.
LLMs encode hierarchical semantic relations asymmetrically, with hypernymy being far more robust and redundantly represented than hyponymy.
People prefer XAI explanations that tell them *why* a feature change doesn't alter the outcome, not just *that* it doesn't.
Achieve single-pass alignment of multi-talker speech – a feat previously impossible – by modeling overlaps as shuffles.
LLMs forget up to 60% of facts when summarizing and erode over half of project constraints during iterative compaction, but a simple discrete memory system (KOs) fixes this while slashing costs by 252x.
Simply translating symbolic sign language notations into natural language unlocks significantly better motion generation when conditioning on phonological attributes with CLIP.
A multi-agent LLM system can fuse heterogeneous data sources to accurately classify building ages from satellite imagery, enabling better urban energy planning despite class imbalances in historical building cohorts.
Seemingly sophisticated dense retrieval methods can catastrophically fail at contradiction detection due to "Semantic Collapse," highlighting the surprising effectiveness of a simple, decoupled lexical approach for reliable biomedical QA.
Current machine translation systems exhibit systematic masculine overuse and inconsistent feminine realization when translating from gender-neutral languages, a problem that can now be quantified thanks to a new gold-standard annotation framework.
Graph transformers avoid oversmoothing in deep layers by structurally preserving community information, a theoretical advantage over GCNs revealed through Gaussian process limits.
Instruction tuning can reduce masculine bias in decoder-only MT models, but these models still don't consistently outperform encoder-decoder architectures on gender-specific translation tasks.
Optimizing multilingual training? Shapley values reveal the hidden cross-lingual transfer effects that current scaling laws miss, leading to better language mixture ratios.
Radiologist dictation, combined with foundation models and minimal parameter updates, can achieve state-of-the-art MRI brain tumor segmentation.
LLMs can be systematically shifted from stochastic pattern-matchers to verified truth-seekers using a carefully orchestrated, multi-stage retrieval and verification pipeline.
Forget prompt privacy – your LLM's responses are leaking *enterprise data*, and this paper shows how to quantify and control it.
Ditch quadratic attention bottlenecks: this new transformer variant achieves competitive time-series forecasting with O(N log N) complexity by representing sequence states on a unit circle.
RAG systems can now achieve 8x better PII leakage protection without sacrificing utility or speed, thanks to a novel "Verify-then-Route" paradigm.
Automating surgical patient triage with an LLM achieves 94% sensitivity, but discrepancies reveal more about clinical workflow gaps than AI errors.
Current AI struggles to understand human values in real-world news events, often missing the who, what, and why – until now.
Mimicking human cognition, FLAIR lets dialogue models "think while listening," boosting performance without adding latency.
LLMs in policing: a seemingly efficient tool that could introduce 17 distinct risks, potentially derailing case progression in over 40 ways.
Students perceive AI assistants as less intimidating and more approachable than human teachers, but also recognize limitations in specialized knowledge and nuanced feedback.
LLMs can disentangle Long COVID pathology from confounding factors like menopause, achieving high precision in predicting disease severity using wearable sensor data.
Forget coding skills, the future of education is teaching "intellectual stewardship"—a framework for humans to responsibly govern AI-augmented knowledge creation.
"Superspreader" networks on Twitter amplify contrarian scientific viewpoints, influencing news media coverage and potentially distorting public understanding of science.
Pre-training on nasal vs. oral context lets a simple model beat large pre-trained speech models at detecting speech disorders in noisy, real-world settings.
Forget complex multi-agent systems: Skele-Code's no-code interface slashes token costs by shifting agent involvement to code generation only, enabling subject matter experts to build agentic workflows directly.
A national center focused on AI and robotics in medicine could be the key to unlocking the transformative potential of these technologies in healthcare.
Control the emotional tone of generated speech without any training by directly manipulating specific neurons within large audio-language models.
Current machine translation systems often fail to capture the nuances of culturally-loaded expressions, highlighting a critical gap in their ability to truly understand and translate language.
LLMs armed with RAG can reconstruct cyberattacks with high precision and recall, but the best model for the job depends on your budget: DeepSeek V3 matches Claude Sonnet 4's accuracy at 1/15th the cost.
Achieve SOTA LLM alignment in complex technical domains with a fraction of the compute by distilling knowledge into smaller models using a hybrid reward mechanism and targeted data augmentation.
Forget chasing leaderboard hype: this study reveals that larger embedding models and strategic concatenation are key to unlocking LLM-powered tabular prediction, regardless of public rankings.
No training needed: ARAM dynamically adjusts retrieved context guidance in masked diffusion models based on signal quality, resolving retrieval-prior conflicts on the fly.
Steganography gets smarter: this framework hides data more effectively by adapting the amount of information concealed in each pixel based on image complexity and payload size.
LLMs don't just change *how* we write, they subtly distort *what* we mean, leading to blander, less insightful, and potentially biased communication.
FrameNet-based semantic annotation unlocks a 30% F1 score boost in detecting gender-based violence from clinical records, outperforming models relying solely on structured data.
LLMs can mimic human lexical patterns, but larger models act like stereotypical humans, sacrificing diversity for typicality in word associations, a trade-off tunable by temperature.
AI's current limitations in adaptability stem from its reliance on psychological learning theories, suggesting a need for representational architectures where systematic behavior is inherent, not accidental.
Generative models can fail to produce globally consistent counterfactuals when causal graphs have complex topologies, but a novel sheaf-theoretic framework with entropic regularization can overcome these limitations.
A simple adaptive normalization technique can significantly improve continual learning performance on tabular data by mitigating catastrophic forgetting in dynamic environments.
Discover emergent narratives in real-time without predefined labels, revealing how information evolves during crises.
LLMs acting as semantic interfaces to our brains pose unprecedented ethical risks to mental autonomy and neurorights, demanding a new "second-order neuroethics."
AI-generated text detectors that seem perfect in the lab fall apart in the real world, with no single method generalizing across domains or even different LLMs.
You can now audit multi-agent LLM systems and trace responsibility for harmful outputs even without access to internal execution logs, thanks to a clever "self-describing text" technique.
Transformer LMs learn linguistic abstractions before memorizing specific lexical items, mirroring key aspects of human language acquisition.
LLMs can now recommend drugs with state-of-the-art accuracy by synthesizing individual patient context with the prescribing tendencies of similar cases, outperforming guideline-based and similar-patient retrieval methods.
Training LLMs to reconstruct arguments boosts their critical thinking abilities across diverse tasks, suggesting a promising new direction for imbuing reasoning skills.
Twitter data reveals a stark linguistic divide in attention towards Ukraine, with distinct clusters emerging around the 2014 and 2022 Russian invasions, mirroring national readiness to support Ukraine.
Truth Social isn't just another right-leaning echo chamber; it's a grievance-fueled narrative machine, while Reddit's conservative corners still cling to policy debates.
LLMs struggle with code comprehension, but a simple RNN pass over their embeddings can boost accuracy by over 5%.
By mapping permutations to a continuous space of "soft ranks," this new diffusion approach makes learning permutation distributions far more tractable, especially for long sequences.
By adaptively calibrating facts and augmenting emotions, FACE-net overcomes the factual-emotional bias that plagues emotional video captioning.
LLMs can now infer plausible stage layouts from unstructured text alone, opening up new possibilities for automated media production.
Forget subjective scouting reports: this framework objectively identifies undervalued football players by blending market dynamics with news sentiment, offering a data-driven edge in talent acquisition.
Forget retargeting: RoboForge's physics-optimized pipeline lets humanoids nail text-guided locomotion with better accuracy and stability.
Surprisingly, you can achieve smooth, controllable image editing in text-to-image models without any training, just by intelligently nudging the text embeddings.
By focusing on semantic differences between scans, DiffVP lets LLMs generate more accurate CT reports without needing explicit lesion localization.
Forget static honeypots – LLMs and RL could make cyber deception dynamic and adaptive, turning the tables on attackers in contested environments.
Security patch detectors trained on standard vulnerability databases are practically useless in the real world, losing up to 90% F1-score when deployed on in-the-wild data.
Existing threat models fail to capture the unique vulnerabilities of Model Context Protocol systems, but MCP-38 fills this gap with a comprehensive taxonomy of 38 distinct threat categories.
Multilingual transformers spontaneously learn a geometric representation of language distance, and we can extract it to improve low-resource translation.
Digital literacy gaps shrink as a browser extension slashes information retrieval time by 87% using an AI-powered tooltip that defines technical acronyms on demand.
Oral exams, previously impossible to scale, can now be delivered for pennies using voice AI, but controlling LLM behavior requires architectural guardrails, not just clever prompts.
By learning bidirectional causal relationships between visual and attribute features, MSDN++ significantly boosts zero-shot learning performance, achieving state-of-the-art results on standard benchmarks.
LLMs can achieve state-of-the-art Alzheimer's detection by mimicking clinical cognitive assessment protocols, not just learning statistical patterns.
Guaranteeing robot safety and task completion just got easier: this method enforces complex temporal logic constraints on pre-trained robotics models without any fine-tuning.
By handling input noise directly through Wasserstein distances, \PWAGPs offer a more robust and transparent approach to uncertainty quantification in GP regression compared to latent-input models.
A 7B model, fine-tuned with a novel inverse specification reward, can generate slide presentations rivaling those of much larger models, highlighting the importance of instruction adherence and tool use over raw parameter count.
Normalizing flows can flag anomalous relationships in scene graphs with 10% better accuracy and 5x faster speed than existing methods, while also exhibiting superior robustness to semantic variations.
Even without pre-loaded database schemas, a new RL agent matches or beats state-of-the-art text-to-SQL models that have full schema access.
Feature models, often treated as static configuration spaces, reveal hidden structural patterns and domain-specific deviations when viewed through the lens of network analysis.
Language models can learn directly from real-world user interactions, boosting performance without human annotations or simulated environments.
Instruction-tuned LLMs can nearly match supervised baselines on complex Arabic morphosyntactic tagging and dependency parsing, but only with careful prompt engineering and retrieval-based in-context learning.
