LLM Agents |

Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell

Wed, 30 Sep 2026 00:00:00 +0000

Edge LLM Inference via Chain of Agents

Mon, 01 Jun 2026 00:00:00 +0000

A Chain-of-Agents approach to memory-efficient large language model inference on resource-constrained edge devices. Accepted as a poster at the NeurIPS 2026 Workshop on Machine Learning for Systems (MLForSys): Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell ( ). Agent decomposition bounds peak KV cache per query about 60% below single-pass and RAG baselines at accuracy parity; the persistent memory tier costs a little cache and buys no measurable accuracy on single-question benchmarks.

Synthony

Tue, 31 Mar 2026 00:00:00 +0000

An agent that recommends the best-fit tabular generative model (GAN / VAE / Diffusion / DP-based / Bayesian / Normalizing Flow) for a given dataset by profiling its “stress” characteristics — privacy sensitivity, class imbalance, cardinality, sample size — instead of defaulting to one architecture. Implements rule-based, LLM-based, and hybrid ranking strategies with explainable tie-breaking and benchmark-driven comparison. Published at ICLR 2026 DeLTA.

SYNTHONY: A Stress-Aware, Intent-Conditioned Agent for Deep Tabular Generative Models Selection

Tue, 31 Mar 2026 00:00:00 +0000

SYNTHONY treats synthesizer selection as a matching problem: it profiles a dataset’s “stress” — privacy sensitivity, class imbalance, cardinality, and sample size — and matches that profile against a calibrated capability registry of synthesizer families (GAN, VAE, Diffusion, DP-based, Bayesian, Normalizing Flow). It supports rule-based, LLM-based, and hybrid ranking with explainable tie-breaking and benchmark-driven comparison.