· ai
[Paper] Large Causal Models from Large Language Models
We introduce a new paradigm for building large causal models (LCMs) that exploits the enormous potential latent in today's large language models (LLMs). We desc...
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- · ai
[Paper] ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning
Large language models (LLMs) are increasingly deployed in settings where reasoning, such as multi-step problem solving and chain-of-thought, is essential. Yet, ...
- · ai
[Paper] On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
Recent reinforcement learning (RL) techniques have yielded impressive reasoning improvements in language models, yet it remains unclear whether post-training tr...
- · ai
[Paper] Distribution Matching Variational AutoEncoder
Most visual generative models compress images into a latent space before applying diffusion or autoregressive modelling. Yet, existing approaches such as VAEs a...
- · ai
[Paper] Mary, the Cheeseburger-Eating Vegetarian: Do LLMs Recognize Incoherence in Narratives?
Leveraging a dataset of paired narratives, we investigate the extent to which large language models (LLMs) can reliably separate incoherent and coherent stories...
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[Paper] Automated Generation of Custom MedDRA Queries Using SafeTerm Medical Map
In pre-market drug safety review, grouping related adverse event terms into standardised MedDRA queries or the FDA Office of New Drugs Custom Medical Queries (O...
- · ai
[Paper] HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMs
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in vision-language understanding tasks. While these models often produce ling...
- · ai
[Paper] When Large Language Models Do Not Work: Online Incivility Prediction through Graph Neural Networks
Online incivility has emerged as a widespread and persistent problem in digital communities, imposing substantial social and psychological burdens on users. Alt...
- · ai
[Paper] Bridging Code Graphs and Large Language Models for Better Code Understanding
Large Language Models (LLMs) have demonstrated remarkable performance in code intelligence tasks such as code generation, summarization, and translation. Howeve...
- · ai
[Paper] Algorithm-hardware co-design of neuromorphic networks with dual memory pathways
Spiking neural networks excel at event-driven sensing yet maintaining task-relevant context over long timescales. However building these networks in hardware re...
- · ai
[Paper] AutoICE: Automatically Synthesizing Verifiable C Code via LLM-driven Evolution
Automatically synthesizing verifiable code from natural language requirements ensures software correctness and reliability while significantly lowering the barr...
- · ai
[Paper] How Do LLMs Fail In Agentic Scenarios? A Qualitative Analysis of Success and Failure Scenarios of Various LLMs in Agentic Simulations
We investigate how large language models (LLMs) fail when operating as autonomous agents with tool-use capabilities. Using the Kamiwaza Agentic Merit Index (KAM...