Tencent (China)

Multi-view 3D vision-language models suffer viewpoint-induced semantic drift: the same object and query produce weaker, diffuse or shifted target responses when the camera moves. LieVLM has geometry tokens predict local rotation parameters and channel-wise gates, applies length-preserving rotations to projected visual features within learned three-dimensional subspaces, and gates the identity-relative correction back into the original representation. On ScanRefer, LieVLM-8B reaches 64.5 Acc@0.25 and 57.2 Acc@0.5, 6.9 and 6.3 points above the published VG LLM-8B under the same proposal-refinement protocol.

Multi-view 3D vision-language models suffer viewpoint-induced semantic drift: the same object and query produce weaker, diffuse or shifted target responses when the camera moves. LieVLM has geometry tokens predict local rotation parameters and channel-wise gates, applies length-preserving rotations to projected visual features within learned three-dimensional subspaces, and gates the identity-relative correction back into the original representation. On ScanRefer, LieVLM-8B reaches 64.5 Acc@0.25 and 57.2 Acc@0.5, 6.9 and 6.3 points above the published VG LLM-8B under the same proposal-refinement protocol.

Möbius places the entire denoising process of motion generation inside the same multimodal representation space used for motion understanding, so clean motion, noisy motion and text share attention without a discrete tokenizer or a pretrained motion VAE. On HumanML3D, the 0.6B variant outperforms diffusion-based, language-model-based and unified motion-language baselines on both text-to-motion generation and motion captioning; scaling to 1.7B parameters and 1,700 hours of motion data improves all motion-language tasks further.

Möbius places the entire denoising process of motion generation inside the same multimodal representation space used for motion understanding, so clean motion, noisy motion and text share attention without a discrete tokenizer or a pretrained motion VAE. On HumanML3D, the 0.6B variant outperforms diffusion-based, language-model-based and unified motion-language baselines on both text-to-motion generation and motion captioning; scaling to 1.7B parameters and 1,700 hours of motion data improves all motion-language tasks further.

Verifiable-reward reinforcement learning hits a learning cliff on problems beyond the model's reach: when a rollout group is entirely wrong, group-relative advantage collapses to zero and the update carries no signal. PICO triggers distribution-constrained joint decoding only on all-failure groups, intersecting the teacher's next-token candidates with the student's while a dynamic cutoff keeps the sampling distribution inside a finite divergence budget, yielding trajectories that pass verification and stay close to the current policy. On Qwen3-4B-Base and Qwen3-8B-Base, PICO averages 32.0 and 35.4 across AIME25, AIME26, HMMT26, MATH500, GPQA-Diamond and HLE, against 27.9 and 31.8 for GRPO.

Verifiable-reward reinforcement learning hits a learning cliff on problems beyond the model's reach: when a rollout group is entirely wrong, group-relative advantage collapses to zero and the update carries no signal. PICO triggers distribution-constrained joint decoding only on all-failure groups, intersecting the teacher's next-token candidates with the student's while a dynamic cutoff keeps the sampling distribution inside a finite divergence budget, yielding trajectories that pass verification and stay close to the current policy. On Qwen3-4B-Base and Qwen3-8B-Base, PICO averages 32.0 and 35.4 across AIME25, AIME26, HMMT26, MATH500, GPQA-Diamond and HLE, against 27.9 and 31.8 for GRPO.

Large reasoning models often keep deliberating after enough information is available, raising decoding latency and memory cost. LoCo moves the compression decision to individual reasoning states: it finds candidate overthinking states in the model's own traces, freezes that prefix, regenerates shorter continuations whose answers verify as correct, and learns a continue-or-conclude preference with DPO. Across five math benchmarks, completion length drops 41.7%–45.4% while accuracy holds or improves; trained on math only, the same model cuts length by 23.4%–36.3% on three code benchmarks.

Large reasoning models often keep deliberating after enough information is available, raising decoding latency and memory cost. LoCo moves the compression decision to individual reasoning states: it finds candidate overthinking states in the model's own traces, freezes that prefix, regenerates shorter continuations whose answers verify as correct, and learns a continue-or-conclude preference with DPO. Across five math benchmarks, completion length drops 41.7%–45.4% while accuracy holds or improves; trained on math only, the same model cuts length by 23.4%–36.3% on three code benchmarks.

Large language models store part of their factual knowledge in hash tables keyed by preceding token sequences, and low-bit quantization erases long-tail facts first, while giving more bits to frequently accessed rows does worse than choosing at random. GateRQ stores each row as a ladder of residual codes, computes each lookup's gate from a 1-bit code, and reads deeper levels only for the lookups the gate ranks highest. On DeepSeek-V4.1-Flash it cuts table memory from 23.6 to 3.04 GiB per GPU and raises vLLM throughput over FP8 tables in GPU memory by about 11% and about 31% on the two workloads, losing about 1 PopQA point at 1.50 bits per lookup.

Large language models store part of their factual knowledge in hash tables keyed by preceding token sequences, and low-bit quantization erases long-tail facts first, while giving more bits to frequently accessed rows does worse than choosing at random. GateRQ stores each row as a ladder of residual codes, computes each lookup's gate from a 1-bit code, and reads deeper levels only for the lookups the gate ranks highest. On DeepSeek-V4.1-Flash it cuts table memory from 23.6 to 3.04 GiB per GPU and raises vLLM throughput over FP8 tables in GPU memory by about 11% and about 31% on the two workloads, losing about 1 PopQA point at 1.50 bits per lookup.