Research Papers Topics
Models and evaluations for reasoning over very large text, audio, video, or code contexts.
Agent Memory · National University of Singapore
EvoArena turns static agent tasks into evolving chains and finds current agents average only 39.6% accuracy; EvoMem adds patch memory and improves chain-level accuracy by 3.7 points.
Multimodal Models · Kuaishou Technology
Kwai Keye-VL-2.0 is a 30B-A3B open MoE multimodal model with 256K context, strong long-video scores, and 62.0 on SWE-bench Verified.
MiniMax Sparse Attention keeps only 2,048 selected KV tokens per query group and reports 28.4x lower attention FLOPs plus 14.2x prefill speedup at 1M context.
Long Context · Tsinghua University
Tsinghua's LongTraceRL mines distractors from real search-agent trajectories and adds entity-level rubric rewards, lifting a Qwen3-4B reasoner from 53.3 to 59.0 average across five long-context benchmarks (+5.7).
AI Agents · Independent Researcher
When Masking Stale Observations Helps Search Agents turns context management for search agents into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
Multimodal Models · Shanghai AI Laboratory
OVO-S-Bench: Streaming Spatial Intelligence in MLLMs turns streaming spatial intelligence into a checkable test, with concrete failure signals, benchmark limits, and builder takeaways.
FlashMemory-DeepSeek-V4 keeps only the KV chunks a neural indexer predicts you will need, shrinking physical KV cache to 13.5% of full-context decoding while accuracy stays flat or edges up ~0.6%.
Efficient AI · Alibaba Qwen Team
RTPurbo converts a trained full-attention LLM into a sparse one with about 600+600 adaptation steps, keeping LongBench accuracy (54.24 vs 53.80) while hitting 9.36x prefill speedup at 1M context.
Long Context · University of Illinois Urbana-Champaign
Ctx2Skill is a self-play framework that discovers natural-language skills from a long context with no human labels or external rewards, lifting GPT-4.1 from 11.1% to 16.5% and GPT-5.1 from 21.2% to 25.8% on CL-bench.
KVarN compresses the KV-cache to 2 bits with no calibration data, using a Hadamard rotation plus dual-axis variance normalization to stop quantization errors from snowballing across long reasoning chains.
Long Context · Shanghai AI Laboratory
δ-mem bolts a tiny 8×8 delta-rule memory onto a frozen LLM and lifts average long-memory scores 1.10× over the backbone and 1.15× over other memory methods — no fine-tuning, no context extension.
Long Context · Google DeepMind
Gemini 1.5 Pro and Flash keep >99% retrieval recall up to at least 10M tokens of text, video, and audio — and Pro matches Gemini 1.0 Ultra with far less compute.
Sequence Modeling · Carnegie Mellon University
Mamba makes state space model parameters depend on the input, so it selectively remembers or forgets tokens. It scales linearly, runs 5x faster than Transformers, and Mamba-3B matches Transformers twice its size.