Search papers, labs, and topics across Lattice.
45 papers published across 5 labs.
Requirements volatility doesn't just delay projects; it directly undermines software architecture, leading to technical debt and scheduling nightmares.
Unlock geometric algebra's performance potential in neural networks and spatial computing by compiling directly from multi-way relationships, eliminating manual specialization and ensuring geometric correctness.
Training domain-specific coding LLMs with realistic environments and large-scale RL can yield substantial gains in practical software engineering tasks.
Autonomous coding agents can now outperform expert-engineered attention kernels on NVIDIA's latest Blackwell GPUs, discovering optimizations that eluded human experts.
LLM-powered security tools are surprisingly susceptible to confirmation bias, overlooking reintroduced vulnerabilities when pull requests are framed as security improvements.
Training domain-specific coding LLMs with realistic environments and large-scale RL can yield substantial gains in practical software engineering tasks.
Autonomous coding agents can now outperform expert-engineered attention kernels on NVIDIA's latest Blackwell GPUs, discovering optimizations that eluded human experts.
LLM-powered security tools are surprisingly susceptible to confirmation bias, overlooking reintroduced vulnerabilities when pull requests are framed as security improvements.
Most sparse tensor compilers are riddled with bugs, silently miscompiling code or crashing on valid inputs, a problem exposed by a new fuzzer that guarantees valid tensor contractions.
Despite advances in LLMs, human-AI collaboration still significantly outperforms AI-only agents in domain-specific data science tasks, proving that human expertise remains crucial.
LLMs analyzing binaries aren't just spitting out tokens – they're exhibiting surprisingly structured reasoning patterns like "early pruning" and "targeted backtracking" that could revolutionize how we understand and control these systems.
Ditch the syntax-only grind: a multi-modal assessment strategy proves that introductory programming courses can boost both coding skills and crucial soft skills like communication and critical thinking.
Current Python vulnerability scanners miss millions of vulnerable downloads by failing to account for vendored dependencies and OS-level security patches.
LLMs can automate and significantly improve the generalization of compiler peephole optimizations, outperforming specialized program synthesis techniques.
Forget months of manual coding: AutORAN lets you build and deploy O-RAN xApps from natural language in minutes.
Imagine a debugger that not only shows you the past, but also lets you explore alternative code paths and their execution, all in real-time.
The complex JS-Wasm boundary is fertile ground for new vulnerabilities, and Weaver is the first fuzzer to effectively till it.
Forget struggling with cryptic SQL: a new LLM fine-tuned with human preferences generates comments so good, they beat Qwen3-14B by up to 13% on standard metrics.
Julia can now hang with the big dogs: KernelForge.jl proves that portable, JIT-compiled GPU primitives can achieve vendor-level performance (matching or exceeding CUB and cuBLAS) without sacrificing generality.
Agentic AI systems are still far from maximizing hardware potential: SOL-ExecBench reveals a significant gap between current GPU kernel performance and analytically derived Speed-of-Light bounds across a wide range of AI models.
A 30B MoE model can now achieve Gold Medal-level performance in IMO, IOI, and ICPC, rivaling frontier models with 20x more parameters.
LLMs can orchestrate complex wireless communication optimization tasks by translating natural language intent into actionable spatial constraints, enabling gradient-based solvers to outperform traditional methods without requiring domain-specific fine-tuning.
ChatGPT-4o-mini can spot design discussions in code repositories better than other models, offering a new path to automatically surfacing valuable context for software engineers.
Semantic sorting in LLMs can be twice as fast with no loss in accuracy by strategically combining listwise ranking algorithms.
Current LMMs can't reliably turn complex images into code, failing to preserve structural integrity even in relatively simple scenarios, as shown by the new Omni-I2C benchmark.
Software architecture, a critical but underspecified domain, finally gets a unified benchmarking platform with ArchBench, enabling standardized evaluation of LLMs on complex system design tasks.
Requirements volatility doesn't just delay projects; it directly undermines software architecture, leading to technical debt and scheduling nightmares.
LLMs can now automatically generate bug-detection patterns for scientific code, offering a scalable solution to the growing problem of methodology errors in AI-driven research.
Achieve up to 2.4x speedup over OpenBLAS on RISC-V by using MLIR and xDSL to generate optimized RVV code, finally unlocking the potential of RISC-V vector extensions.
Simply prompting for test-driven development can *increase* regressions in AI coding agents; instead, focus on surfacing contextual information about which tests are most relevant.
LLMs can read datasheets, but still can't design circuits, failing at basic physical intuition despite showing promise in documentation understanding.
Random walks and equitable partitions offer a fresh perspective on bounding the smoothing parameter in code-based cryptography, potentially surpassing Fourier transform-based methods.
Automated injection of realistic vulnerabilities and synthesis of PoV exploits finally makes scalable, precisely labeled, repository-level vulnerability datasets a reality.
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.
Unlock geometric algebra's performance potential in neural networks and spatial computing by compiling directly from multi-way relationships, eliminating manual specialization and ensuring geometric correctness.
Despite the ease of integrating ML cloud services, developers are widely misusing them, leading to quality and maintainability issues that MLmisFinder can now automatically detect with high accuracy.
Forget about chasing the perfect model architecture – this work suggests the real key to better AI agents lies in crafting more precise and complete specifications, since the implementation can always be re-generated.
LLMs can't reason their way through Rust verification, struggling to complete proofs even with substantial hints, revealing a critical gap in their ability to handle the rigorous demands of secure software development.
A 4B parameter model can nearly match the privilege escalation performance of a state-of-the-art closed LLM like Claude Opus, while being fully local and 100x cheaper to run.
Standardized, modular GenAI teaching units in GUIDE offer a practical path to integrating cutting-edge AI tools into digital design education.
Secure enclave updates and migrations, previously missing from RISC-V TEEs, are now practical thanks to a novel toolkit that adds minimal overhead.
LLMs struggle with code comprehension, but a simple RNN pass over their embeddings can boost accuracy by over 5%.
Finally, a software energy profiler achieves both high accuracy and cross-platform portability, enabling practical algorithmic energy optimization across diverse languages and hardware.
Forget prompt engineering: AgentFactory lets LLM agents self-evolve by accumulating and refining executable Python subagents, making task re-execution more reliable and efficient.
Turning past programming failures into reusable knowledge boosts automated repair performance by 3.7% on a multimodal benchmark.
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.
Genetic programming can discover unconventional multigrid cycles that outperform hand-tuned methods, suggesting automated algorithm design can unlock untapped performance in classical numerical solvers.
Federated Computing as Code lets you enforce data sovereignty in federated systems with cryptographic guarantees, moving beyond runtime policies and trust assumptions.
Forget specialized tools: a standard Unix terminal and clever RL are all you need to beat much larger LLMs at code search.
LLMs can now generate Verilog code that's not just correct, but also optimized for real-world hardware constraints like power, performance, and area, thanks to a novel multi-agent system with evolving memory.
