Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
Advances in Neural Information Processing Systems , volume =
7 Pith papers cite this work. Polarity classification is still indexing.
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A solvable hierarchical model with power-law feature strengths yields explicit power-law scaling of prediction error through sequential recovery of latent directions by a layer-wise spectral algorithm.
SDG-MoE introduces learned signed interaction graphs and disagreement-gated deliberation among experts in MoE architectures, yielding 19.8% better validation perplexity than the strongest baseline.
Domain-camouflaged injection attacks reduce detection rates from 93.8% to 9.7% on Llama 3.1 8B and 100% to 55.6% on Gemini 2.0 Flash, with the gap persisting in production classifiers and multi-agent debate setups.
NeuroMAS reframes multi-agent language systems as neural architectures where LLM agents learn coordination via reinforcement learning rather than predefined roles.
DWT decomposes sentence- or word-level embeddings into multi-resolution components that preserve semantics for direct or LLM-guided summarization, yielding up to 97% fidelity and gains in BERTScore and semantic metrics over GPT-4o baselines on clinical and legal benchmarks.
Proposes the pre/post-training boundary as the basis for IP division in industry-academia ML collaborations via the PBOS contract template.
citing papers explorer
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Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
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Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model
A solvable hierarchical model with power-law feature strengths yields explicit power-law scaling of prediction error through sequential recovery of latent directions by a layer-wise spectral algorithm.
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SDG-MoE: Signed Debate Graph Mixture-of-Experts
SDG-MoE introduces learned signed interaction graphs and disagreement-gated deliberation among experts in MoE architectures, yielding 19.8% better validation perplexity than the strongest baseline.
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Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems
Domain-camouflaged injection attacks reduce detection rates from 93.8% to 9.7% on Llama 3.1 8B and 100% to 55.6% on Gemini 2.0 Flash, with the gap persisting in production classifiers and multi-agent debate setups.
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NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
NeuroMAS reframes multi-agent language systems as neural architectures where LLM agents learn coordination via reinforcement learning rather than predefined roles.
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DWTSumm: Discrete Wavelet Transform for Document Summarization
DWT decomposes sentence- or word-level embeddings into multi-resolution components that preserve semantics for direct or LLM-guided summarization, yielding up to 97% fidelity and gains in BERTScore and semantic metrics over GPT-4o baselines on clinical and legal benchmarks.
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Position: The Pre/Post-Training Boundary Should Govern IP in Industry-Academia ML Collaborations
Proposes the pre/post-training boundary as the basis for IP division in industry-academia ML collaborations via the PBOS contract template.