Search papers, labs, and topics across Lattice.
48 papers published across 5 labs.
Achieve near-native Indic TTS from a non-Indic base model at zero commercial-training-data cost by cleverly combining phoneme space unification, LoRA adaptation, and voice-prompt recovery.
Democratizing self-driving research, OpenPodcar2 offers a robust, low-cost (≈$7k new, $2k used), open-source autonomous vehicle platform ready for ROS2 integration and real-world deployment.
Meta's risk assessment of its Code World Model (CWM) gives it a clean bill of health, concluding it poses no *new* catastrophic risks beyond those already present in the AI landscape.
Unlock advanced robotic manipulation with FlexiTac, a tactile sensing solution so cheap and easy to integrate, you'll wonder why you were using anything else.
Hyperbolic embeddings are powerful, but a fragmented ecosystem makes them hard to use—this framework finally puts them all in one place.
Meta's risk assessment of its Code World Model (CWM) gives it a clean bill of health, concluding it poses no *new* catastrophic risks beyond those already present in the AI landscape.
Unlock advanced robotic manipulation with FlexiTac, a tactile sensing solution so cheap and easy to integrate, you'll wonder why you were using anything else.
Hyperbolic embeddings are powerful, but a fragmented ecosystem makes them hard to use—this framework finally puts them all in one place.
Forget computationally verifying stability – VibroML automatically *fixes* dynamically unstable crystal structures, opening the door to exploring previously inaccessible materials.
Even with emotion-aware prompting, today's best small language models still struggle to preserve subtle emotional nuances when translating between languages.
Forget turn-based interactions: MiniCPM-o 4.5 lets you build AI that sees, hears, speaks, and *reacts* in real-time, all on a device with only 12GB of RAM.
Newcomers beware: the odds of your "good first issue" pull request getting merged have plummeted nearly 20% in the last year.
Forget Shakespeare, LLMs can now sling verses in Arabic dialects, thanks to a new dataset for instruction-guided poetry generation.
LLMs exhibit surprisingly human-like biases and overconfidence in math, revealed by a new dataset mapping their mathematical reasoning across diverse personas.
Thai voice cloning just leapfrogged human performance on short-duration speech, thanks to a new model that directly handles code-switching and numerals.
LLM-powered query reformulation, a hot topic in IR, often fails to translate gains from lexical to neural retrieval, and bigger models don't always help.
LLM upgrades are a chaotic mix of progress and decay: despite overall gains, up to 47% of questions get *worse* after an update, and single-shot evals miss almost half of these critical regressions.
LLMs trained on raw code text learn surface-level cues that trigger false positives when detecting vulnerabilities in other languages, but simply feeding them ASTs at inference time can dramatically reduce these errors.
You can steal secrets from locally fine-tuned LLMs by backdooring their model code, even bypassing common defenses like differential privacy and code audits.
"Utility" code, intended to be broadly useful and reusable, is actually 2.75x more likely to be involved in a vulnerability than other code.
Defining "hero developers" in open-source projects is more nuanced than previously thought: technical prowess doesn't guarantee social engagement, and vice versa, impacting bug-fixing success in surprising ways.
Reproducibility issues plague over 20% of Defects4J, a widely used benchmark for automated program repair, casting doubt on the validity of many APR evaluations.
Replaying CI failures in embedded systems is now possible at scale: PhantomRun reconstructs over 90% of failing builds, opening the door to systematic debugging and failure analysis.
You can slash false positives in PyPI malware detection by 82% while simultaneously reducing feature dimensionality by 50% using a carefully tuned deep learning approach.
AI agents and humans exhibit over 10 distinct repair behaviors when performing urgent hot fixes, suggesting opportunities for targeted human-automation collaboration.
NVIDIA's closed-source driver secrets are out: researchers can now see the exact hardware commands triggered by CUDA code.
Complex, multi-step instructions can cause LLMs to completely ignore question content and instead rely on positional shortcuts when asked to underperform, revealing a critical vulnerability in adversarial evaluation.
Forget giant LLMs: fine-tuned small language models can actually *beat* GPT-4o on critical clinical tasks like emergency triage.
Non-linear scoring with Hypencoders boosts retrieval performance, but don't expect it to fix your speed or adversarial robustness problems.
Sanctions and censorship breed a shadow economy: Iranian third-party iOS app stores are rife with cracked apps, unauthorized monetization, and privacy-invading trackers.
Local LLMs can now rival cloud-based giants like GPT-4o in Linux privilege escalation tasks, thanks to targeted system-level and prompting interventions.
SMBs drowning in security logs can now achieve enterprise-grade threat detection with a lightweight, open-source framework fine-tuned on a tiny LLM.
Post-release software bugs aren't just about code complexity; they're a symptom of code age, frequent modification, and high churn, demanding a shift in testing focus.
Open-source LLM agents can get a 27% performance boost in tool use by strategically injecting context tailored to address their most common failure modes.
Achieve near-native Indic TTS from a non-Indic base model at zero commercial-training-data cost by cleverly combining phoneme space unification, LoRA adaptation, and voice-prompt recovery.
Multilingual MoEs can achieve best-in-class performance-to-compute ratios, even with extreme sparsity, by strategically upcycling from dense models and exhibiting structured expert activation patterns across languages.
Plug-and-play multi-agent systems are now a reality: OxyGent's "Lego-like" abstraction lets you compose agents, tools, and LLMs into scalable systems with unprecedented observability and evolvability.
Fresh masking between pipeline stages in NTT-based post-quantum crypto isn't just good practice, it's provably necessary to erase vulnerabilities arising from prior stages, as demonstrated with a machine-checked proof and a real-world hardware flaw.
EOS-Bench reveals that the complexity of satellite scheduling can be systematically quantified, unlocking new insights into algorithm performance across thousands of scenarios.
SlicerRoboTMS revolutionizes Robo-TMS research by providing a versatile, open-source platform that simplifies integration and enhances reproducibility.
Finally, a plugin framework that lets you mix-and-match KV-Cache, LoRA, and other controls to steer diffusion models without being locked into a specific backbone.
See where your citations are coming from with a single command, thanks to CiteRadar's open-source platform that automatically generates interactive maps and detailed researcher profiles from your Google Scholar ID.
On-device SLMs in mobile apps demand a radical shift: the less the LLM does, the more reliable it becomes.
Go's security-critical infrastructure is riddled with thousands of cryptographic API misuses, and your favorite static analysis tool might be missing them.
5G emergency alert systems are surprisingly vulnerable to spoofing attacks that can do more than just display fake warnings.
More reviewer bot comments on agentic pull requests actually *increase* resolution time, suggesting that quality trumps quantity in automated code review.
OSS developers who saw automatically generated user personas responded to issues with more empathy and tailored explanations, suggesting a simple UI intervention can bridge the user-developer gap.
Open-source library vulnerabilities are easier to spot when you connect the dots between bug reports, code changes, and commit messages.
Cytogeneticists can now slash chromosome analysis time from days to seconds with Aycromo, an open-source platform that democratizes access to high-performance deep learning models.
An open-source autonomous driving platform offers researchers a modular, scalable, and cost-effective alternative to complex and restrictive hardware validation setups.
Democratizing self-driving research, OpenPodcar2 offers a robust, low-cost (≈$7k new, $2k used), open-source autonomous vehicle platform ready for ROS2 integration and real-world deployment.
Open-source diffusion models can now achieve state-of-the-art illumination control rivaling closed-source alternatives, thanks to a novel training pipeline and dataset.
Speculative design can effectively catalyze critical reflection and generate actionable insights for fostering designer inclusion within the often developer-centric world of Open Source Software.
