The State of AI Coding 2025 | Greptile

Multi-Head Latent Attention compresses key/value representations into small latent vectors, shrinking KV caches and easing memory pressure.

Sparse MoE routing activates only a few experts per token and limits cross-node communication to keep GPUs fully utilized.

Multi-Token Prediction adds auxiliary targets per token, increasing learning signal density during training.

Overall, the model treats scale as a data-flow and memory-management problem, not just a parameter-count problem.

Time-aligned Multimodal RoPE (TMRoPE) synchronizes audio and video via consistent temporal position embeddings.

Encoders process inputs in blocks, while a central language model handles long-range reasoning and context.

A Thinker–Talker architecture splits responsibilities: Thinker does text reasoning, Talker turns internal representations into streaming speech.

The design highlights that decoupling perception, reasoning, and generation can make multimodal systems easier to scale and debug.

LC tends to outperform RAG on continuous, well-structured sources (books, wiki articles) and precise fact-based questions.

RAG tends to win on fragmented, multi-source, and dialogue-heavy data, especially under loose F1-style scoring.

Summarization-based retrieval performs similarly to LC, while simple chunk-based retrieval falls behind.

The core claim: LC and RAG succeed under different structural assumptions about where relevant information lives.

Inputs are split into fixed-size KV "pages" with bookmark tokens summarizing each page.

A trained page retriever selects important KV pages per layer; offloaded pages live off-device and are pulled back on demand.

Across LongBench, InfiniteBench, and RULER, RetroLM beats standard RAG pipelines and other efficient long-context methods.

The framework reframes retrieval as selecting which cached representations to keep, rather than which raw tokens to stuff into the prompt.

Standard MoA queries multiple different models and aggregates their answers; Self-MoA instead repeatedly samples a single strong model.

An aggregator LLM combines multiple responses from that one model, trading cross-model diversity for in-model diversity.

Experiments on AlpacaEval 2.0 and other benchmarks show Self-MoA outperforming traditional MoA when proposer quality is high.

A sequential variant, Self-MoA-Seq, aggregates in sliding windows to stay within context limits while scaling the number of samples.

The system samples rollouts, has the model analyze its own traces in natural language, and proposes new prompts.

A Pareto front keeps multiple candidate prompts that perform well on different subsets of data.

Across four tasks, GEPA matches or beats GRPO-style RL with up to 35× fewer rollouts.

The work treats prompts as an external optimization layer, using natural-language reflection rather than heavyweight RL.

The agent uses three minimal tools—search_internet, browse_page, stateless code_interpreter—designed to force explicit reasoning.

A self-managed memory tool (clean_memory) lets the agent control long-horizon context instead of passively appending everything.

Length-normalized RL stabilizes multi-step optimization and prevents degenerate, repetitive tool use.

Results on Humanity's Last Exam and related benchmarks highlight that context management and planning are the core bottlenecks, not just model size.

A small Transformer operates purely on similarity-score distributions of candidate passages, predicting a lower and upper similarity bound.

Retrieval becomes selecting a continuous "band" of passages, instead of top-k or independent Bernoulli decisions.

LDAR uses 25–63% of the tokens of long-context baselines while maintaining or improving performance.

The central claim is that context quality and distraction-awareness matter more than raw context size, especially on noisy corpora.

At each step, previous memory and new observations are merged into a compact internal state token (IS), and older context is discarded.

A masked-trajectory RL scheme reconstructs valid trajectories for PPO without feeding the entire history.

MEM1-7B matches or beats much larger baselines on tasks with up to 16 sequential objectives while reducing memory use by ~3.7×.

The work shows that long-horizon behavior can come from learned internal state handling rather than expanding context windows or bolting on external memory.

The framework uses a structured template: think for internal reasoning, search for queries, information for retrieved context, and answer for final output.

PPO or GRPO updates apply only to model-generated segments, treating the search engine as part of the environment.

Evaluated across seven QA datasets, Search-R1 delivers large gains over strong RAG baselines, including on multi-hop tasks like HotpotQA and 2WikiMultiHopQA.

The paper positions targeted, RL-trained search behavior as an alternative to static top-k retrieval and hand-crafted tool chains.