Near-optimal vector quantization from Google's ICLR 2026 paper — 95% recall, 5x compression, zero preprocessing, pure Python FAISS replacement

First open-source KVTC implementation (NVIDIA, ICLR 2026) -- 8-32x KV cache compression via PCA + adaptive quantization + entropy coding

(ICLR 2026 Oral 🔥) Universal Inverse Distillation for Matching Models with Real-Data Supervision (No GANs)

🔬 Official implementation of ExPO-HM: Learning to Explain-then-Detect for Hateful Meme Detection (ICLR 2026). Novel multimodal RL approach for interpretable and explainable content moderation.

[ICLR 2026] Tree-Sliced Sobolev IPM

Code for locating "critical neurons" in LLMs. We show that masking as few as 3 neurons can cripple a model's capabilities (ICLR 2026).

[ICLR 2026] Revisiting Tree-Sliced Wasserstein Distance Through the Lens of the Fermat–Weber Problem

Near-optimal vector quantization for LLM KV cache compression. Python implementation of TurboQuant (ICLR 2026) — PolarQuant + QJL for 3-bit quantization with minimal accuracy loss and up to 8x memory reduction.

Interactive Benchmarking Tool for TurboQuant KV Cache Compression. Supports 2-4 bit quantization with Real-time Metrics

AI agent skill implementing Google's TurboQuant compression algorithm (ICLR 2026) — 6x KV cache memory reduction, 8x speedup, zero accuracy loss. Compatible with Claude Code, Codex CLI, and all Agent Skills-compatible tools.

ICLR 2026 paper on state-dependent reward shaping with multi-view videos and ViCLIP for reinforcement learning.

Trace and debug AI agent behavior locally with a step-by-step visual tool that stores data offline for clear inspection and faster development.

Visualize and compare reinforcement learning algorithms for LLM training with interactive formulas, pipeline flow, and algorithm metrics.

🌐 Discover semantically similar AI research papers from ICLR 2026 and other top venues, simplifying your search for relevant academic content.