AI/ML Research Digest — Apr 11, 2026

LLM inference efficiency via adaptive routing, pruning, and hardware‑aware scaling

Dynamic routing that selects full or sparse attention per layer cuts the cost of long‑context processing. Flux Attention implements this routing and delivers 2–3× speedups on benchmark tasks while keeping accuracy within a few points [1].

When routing is paired with token‑level pruning, the gains multiply. A task‑conditioned pruning network discards 92 % of input tokens that are irrelevant for the next action, yet it preserves recall and F1 scores [2].

Both techniques are hardware‑aware: QEIL v2 replaces hand‑tuned heuristics with a physics‑based metric and a simulated‑annealing optimizer. On an 8B model the optimizer lowers inference energy by 75.6 % and latency by 38.3 % [3].

Why it matters: inference cost dominates deployment budgets for large models. The three papers together show a practical path to halve compute, cut energy, and still run demanding long‑context applications.

Reinforcement learning for robust reasoning and skill evolution

Group‑relative policy optimisation (GRPO) reshapes reward distributions so that gradients are balanced across tasks. The Gaussian variant enforces equity at the level of reward statistics, which translates into state‑of‑the‑art scores on multimodal reasoning benchmarks [4].

A related line builds reusable behaviours from large trajectory archives. By retrieving skill primitives from a growing library and stitching them hierarchically, agents acquire new capabilities without retraining from scratch [5].

The deontic‑reasoning benchmark reveals a blind spot: standard fine‑tuning struggles with rule‑based tasks, and RL fine‑tuning only yields modest improvements [6]. This underscores the need for optimisation methods that respect logical constraints, exactly what GRPO targets.

Why it matters: robust, consistent reasoning is a prerequisite for trustworthy agents, especially when they must switch between heterogeneous tasks.

Embodied multimodal foundations and spatial video generation

A Mixture‑of‑Transformers (MoT) backbone merges several specialist transformers under a single on‑policy distillation loop. The 32B MoT model matches the performance of larger frontier systems while using roughly half the parameters [7].

Streaming VideoLLM closes the perception‑action loop in real time. Running on two 80 GB accelerators, it processes continuous video at 2 FPS, enabling live question answering over streams [8].

Spatially aware diffusion models generate video conditioned on 3D layout and lighting, giving users direct control over scene geometry and illumination [9].

Why it matters: embodied agents need models that can both perceive and act continuously; compact MoT architectures and real‑time video generation make such agents feasible on current hardware.

Token‑efficient representations for scaling vision and language

Trigonometric key‑value compression replaces dense attention maps with compact sinusoidal codes, reducing memory footprints without sacrificing expressivity [10].

ViT token‑space scaling introduces a low‑rank SVD framework that expands token representations while avoiding latent collapse; the closed‑form solution sidesteps costly iterative optimisation [11].

Both techniques produce smaller intermediate tensors, which speeds up training and inference for vision‑language Transformers.

Why it matters: as models grow, token‑level bottlenecks become the dominant barrier; these compression schemes keep scaling affordable.

Standout contributions at a glance

Flux Attention – layer‑wise sparse‑full routing, 2–3× faster long‑context inference [1].
Tool‑output pruning – 92 % token reduction with minimal performance loss [2].
Gaussian GRPO – equitable multi‑task RL, best‑in‑class reasoning scores [4].
Mixture‑of‑Transformers – 32B embodied model, half the parameters of comparable systems [7].
QEIL v2 – physics‑grounded optimizer, cuts energy by three‑quarters [3].
VideoLLM streaming – 2 FPS live video question answering on dual 80 GB GPUs [8].
Deontic‑reasoning benchmark – exposes limits of conventional fine‑tuning, nudging the field toward RL‑based fixes [6].

Together these works map a clear trajectory: smarter routing, aggressive pruning, and physics‑aware scheduling shrink inference costs; robust RL methods tighten reasoning; and token‑level compression sustains growth across vision and language domains.