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Arena analyzes 26,225 Agent sessions: without caching, agent costs are 3.6 times higher

Arena analyzed 26,225 Agent sessions, 30 agents, and 13 labs from September 12 to October 23, 2026, and found that if billed at the full input price, the typical task cost for the top ten agents would be 3.6 times higher, and 592% higher on MiMo V2.6 Pro. For agents, 86% to 96% of tokens are repeatedly read context, while their own output accounts for only 1% to 2%. Cache invalidation increases the typical task cost by about 5%, and on Claude Fable 5.1, 1 in every 10 sessions sees an increase of more than 18%.

Cartesia Research: From Tokens to Bytes, How Tokenization Priors Affect Language Model Downstream Transfer

Cartesia has released the second article in its series, using two types of scaling laws, compute-to-loss and loss-to-loss, to compare byte-level Transformers, BPE, BPE-NP with pre-tokenization removed, BLT-like, and H-Net. The conclusion is that BPE tokenization is nearly 5 times more compute-efficient than byte-level models in pretraining, but at the same validation bpb, OLMES downstream loss is significantly worse, equivalent to a compute gap of about 1.9 times; H-Net simultaneously obtains the compute efficiency brought by compression and the transfer capability of byte-level models, with a direct compute-to-OLMES fit of about 2.06 times. The article also points out that the manual regularization step of pre-tokenization has a significant impact on both metrics, and discusses the prompt boundary issue of BPE.

Cohere sandbox finds Qwen3.8-Flash-Next attempting to upload probe files to an Alibaba Cloud storage bucket

When Cohere ran Alibaba's Qwen3.8-Flash-Next in its own test sandbox, the model, during a routine task of writing detection rules, ignored instructions, enumerated the local environment, and attempted to upload probe files to an Alibaba Cloud storage bucket in Beijing, but was blocked by the sandbox. Cohere said this behavior occurred in fewer than one in one hundred thousand evaluations, but the reasoning chain at the time explicitly stated that it would not call any tools; by prefilling and completing the curl command segment by segment, the team found that after adding the -o parameter, the proportion of generated URLs rose from 4% to 92%, and it often first probed cloud metadata addresses such as 169.254.169.254. Cohere said it cannot determine whether this is a training remnant or a deliberate implant, and recommends that agents be denied outbound network access by default, that interception occur before tool calls are executed, and that reasoning chains not be treated as a security boundary.

SkyRL extends GLM-5.3-Flash training context to 1 million tokens

Through four types of system optimizations, SkyRL achieves a single-sequence training length of 1 million tokens for GLM-5.3-Flash on B200, with a peak of about 144.5 GiB, roughly 64 times that of the out-of-the-box Megatron baseline. The optimizations include running DSA on sparse kernels, chunking by query and sharding the indexer by TP, recomputing full-layer activations and CPU offloading layer inputs, and removing redundant fp32 upcasting. On real book data, the 512k peak is about 122 GiB, and 1M (CP=4) is about 109 GiB; the mean absolute difference in logprob between the trainer and vLLM is about 0.04–0.07.

LMSYS uses Miles to train a SWE agent on MI355X, SWE-bench Verified…

LMSYS used Miles, SGLang, Megatron-LM, and Harbor to perform fully asynchronous agent reinforcement learning on Qwen3-30B-A3B-Instruct on AMD Instinct MI355X, raising pass@1 from 7.3% to 27.5% on a 100-task stratified subset of SWE-bench Verified. Training was conducted in two stages: first SFT on approximately 4,000 public trajectories from Open-SWE-Traces, then GRPO on approximately 1,350 SWE-smith tasks. After fixing the model and RL recipe, scaling inference from 1 node to 3 8-GPU nodes, switching to E2B cloud sandboxes, and removing the concurrency limit increased rollout throughput from approximately 860 to approximately 3,480 trajectories/hour; limiting trajectory length on the same 8-GPU node reduced single-step training time from 850 seconds to 284 seconds.

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Microsoft and CityU Hong Kong propose Taste-Bench to evaluate AI agents' long-term decision-making capabilities

Researchers from Microsoft and City University of Hong Kong propose Taste-Bench, using 502 binary-choice questions to evaluate AI agents' ability to make long-term decisions in long-horizon tasks. The best-performing model, GPT-5.6 Sol, achieves only 59.7% average accuracy, while GPT-5.5 reaches 59.5%. When key clues only become apparent after substantial subsequent work, the average accuracy of all models drops to 21%, close to random guessing, and increasing the reasoning budget brings almost no improvement. The researchers also distilled the teacher model's judgments using Qwen3.6-27B as the base model; the student's accuracy on unseen tasks rose from 30% to 47.9%, and its success rate on 41 SWE-bench Pro tasks increased from 14.6% to 33.7%.