Frontier LLM agents perform complex interactive tasks, but their inference costs motivate transferring their experience to smaller open-source agents. Distillation and latent communication typically require target-model training or access to source-model internals. We study how textual frontier-model trajectories can provide reusable local controls for a frozen agent. Inspired by protocol reverse engineering, we introduce Agentic Latents, comprising anchored states and dynamic profiles of feedback--response cycles elicited by replay in the target model. Our training-free framework, REVEAL (REVerse Engineering Agentic Latents), uses these profiles and trajectory outcomes to match and select local events from successful and failed rollouts of the same task and source model. Their response-transition contrasts yield a target-specific steering direction, applied before each response. Within-benchmark evaluations on Terminal-Bench 2.1 and Humanity's Last Exam with tools report higher accuracy than unmodified agents across three targets. Representation analyses and behavioral case studies support reusing frontier-model experience as local activation control.
Figures & tables
Figure 1: Three routes for reusing frontier-model experience: (1) feature distillation trains on internal states; (2) black-box distillation trains on textual trajectories; (3) REVEAL characterizes Agentic Latents through frozen target-model replay of textual trajectories and derives steering directions.
Figure 2: REVEAL reconstructs activation control from textual frontier-model trajectories. (a) Frozen replay yields Agentic Latents: anchored states and dynamic profiles. (b) Contrastive sample selection identifies matched local events; steering vector calculation combines their response-transition differences through hierarchical averaging. (c) The frozen target applies the resulting direction before each response.
Method
Terminal-Bench 2.1
HLE w/ Tools
Easy
Medium
Hard
All
Math
Phys.
Chem.
Bio/Med.
CS/AI
Eng.
Hum./Soc.
Other
All
Qwen3.6-27B
Vanilla
75.00
43.64
10.00
33.71
24.65
14.00
13.33
20.93
12.70
15.79
12.00
4.44
18.00
Prompt-Reg
100.00
38.18
20.00
34.83
28.37
12.00
13.33
27.91
17.46
15.79
14.00
4.44
20.80
REVEAL-Base
75.00
41.82
20.00
35.96
27.91
20.00
20.00
20.93
19.05
10.53
12.00
6.67
21.00
REVEAL (Ours)
100.00
47.27
16.67
39.33
29.30
16.00
20.00
23.26
19.05
10.53
16.00
8.89
22.00
Table 1: Main agent performance (accuracy, %). Shading is normalized within each column; darker cells indicate higher scores. Within each model block, the best and second-best All scores are bold and underlined, respectively.
Accuracy (%) ↑
Online tokens (k) ↓
Interactions ↓
Target training
Method
Easy
Medium
Hard
All
Input
Output
Rounds
Tool calls
Frontier Model
93.75
60.00
39.17
54.49
346.16
9.46
16.03
28.56
–
Vanilla
75.00
43.64
10.00
33.71
595.14
15.21
23.30
47.91
No
Vanilla + SFT
75.00
49.09
6.67
35.96
223.14
14.02
15.70
34.87
Yes
REVEAL
100.00
47.27
16.67
39.33
143.71
6.95
13.88
27.92
No
Table 2: Accuracy and online interaction costs on Terminal-Bench 2.1. The row in gray reports the frontier model’s mean pass@1 over four rollouts per task; the remaining rows use Qwen3.6-27B. Token and interaction costs are means per task. Bold values indicate the best results among the three target-model methods.
Figure 3: Endpoint approximations of mean response displacement across three models and two benchmarks; points and bars show task-balanced means and 95% task-bootstrap intervals. Layers 23, 22, and 8 are used for Qwen, Gemma, and GPT-OSS, respectively.
Figure 4: Layerwise representation profiles on Terminal-Bench with pointwise 95% task-bootstrap intervals; dashed and solid C curves denote response-start and response-end projections.
Figure 5: Cross-model Spearman correlations of trajectory-level U and S across depth bands, before and after task centering on matched trajectories. E/M/L denote early/middle/late bands.
Figure 6: A case study. REVEAL and the frontier reference compare alternative token-counting operations, whereas Vanilla finalizes without testing the recognized ambiguity.
Appendix figures & tables5 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7: Dual endpoints versus individual response anchors, stratified by response length. Positive values indicate higher cosine similarity or lower relative error against the full-response mean displacement. Bars show paired 95% task-bootstrap intervals; hollow markers denote strata with fewer than three tasks.
Figure 8: Full-depth HLE profiles with pointwise 95% task-bootstrap intervals. Dashed and solid C curves mark response start and end, respectively. U/C/V use 1,303 tasks; S uses 1,091 with valid adjacent updates. Only horizontal depth is rescaled; V excludes unsupported final layers.
Figure 9: Within-response retention at layers 23/22/8 for Qwen/Gemma/GPT-OSS. Lines show 20 relative-position bin means; separate diamonds mark the response-start and response-end anchors. All recorded events with at least 20 content tokens are included. Shading and bars show 95% task-bootstrap intervals.
Figure 10: Task-balanced V and Recall@50 across early/middle/late depth bands on Terminal-Bench and HLE, using supported J-lens layers. Panels use separate linear scales; uniformly random vocabulary ranking has expected V=0.5 .
Figure 11: Cross-model correlations of same-task success–failure contrasts before and after adjustment for response length and analysis-center count. E/M/L denote early/middle/late depth bands. Every panel uses the same 34 Terminal-Bench and 208 HLE tasks; ΔU provides the update-magnitude comparison.
Frontier multimodal large language models (MLLMs) can control robots without robot-specific training, but how their capabilities compare and interact with post-trained vision-language-action (VLA) and world action model (WAM) policies across manipulation demands remains to be thoroughly evaluated. In this report, we evaluate GPT-6-Astra on EBench as the core of an agent that controls a robot through an agent harness under the same visual and proprioceptive observation protocol as post-trained policies. We study two agent configurations: direct control, in which the agent generates waypoint commands directly, and harness with policy, in which it can also invoke a post-trained VLA or WAM policy for action execution. Under direct control, the agent achieves competitive overall performance relative to post-trained policies. It leads on short-horizon tasks and exhibits the least performance variation across generalization dimensions, whereas it underperforms post-trained policies on long-horizon tasks, tabletop manipulation, and high-precision contact-rich tasks. In the harness with policy, invoking a post-trained VLA or WAM combines the complementary strengths of both components, raising overall task success to the level of the strongest compared post-trained policies while substantially reducing token cost. However, performance remains below the per-task upper bound of the standalone components due to suboptimal policy invocation, fixed-instruction execution without subgoals, and grasp disruption when the agent resumes control. Overall, our study characterizes the capability boundary of a frontier MLLM agent and suggests directions for improving future agent harness design. Project page: https://internrobotics.github.io/EBench-Astra-Tech-Report/
Yifei Yao, Yude Zou, Guanbo Jiao +12
Shanghai Artificial Intelligence Laboratory · Shanghai Jiao Tong University · Fudan University +1
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-intensive pipeline spanning pre-training, continual pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL). In this report, we show that when fueled with informative and high-difficulty trajectories, a simple SFT approach could be surprisingly powerful for training frontier search agents. By introducing three simple data synthesis modifications: scaling knowledge graph size for richer exploration, expanding the tool set size for broader functionality, and strict low-step filtering, we establish a stronger baseline. Trained on merely 10.6k data points, our OpenSeeker-v2 achieves state-of-the-art performance across 4 benchmarks (30B-sized agents with ReAct paradigm): 46.0% on BrowseComp, 58.1% on BrowseComp-ZH, 34.6% on Humanity's Last Exam, and 78.0% on xbench, surpassing even Tongyi DeepResearch trained with heavy CPT+SFT+RL pipeline, which achieves 43.4%, 46.7%, 32.9%, and 75.0%, respectively. Notably, OpenSeeker-v2 represents the first state-of-the-art search agent within its model scale and paradigm to be developed by a purely academic team using only SFT. We are excited to open-source the OpenSeeker-v2 model weights and share our simple yet effective findings to make frontier search agent research more accessible to the community.
Frontier LLMs can navigate complex websites, but their cost and reliance on third-party APIs make local deployment impractical. We introduce Agent-as-Annotators, a framework that structures synthetic trajectory generation for web agents by analogy to human annotation roles, replacing the Task Designer, Annotator, and Supervisor with modular LLM components. Using Gemini 3 Pro as teacher, we generate 3,000 trajectories across six web environments and fine-tune a 9B-parameter student with pure supervised learning on the 2,322 that pass quality filtering. The resulting model achieves 41.5% on WebArena, surpassing closed-source models such as Claude 3.5 Sonnet (36.0%) and GPT-4o (31.5%) under the same evaluation protocol, and nearly doubling the previous best open-weight result (Go-Browse, 21.7%). Capabilities transfer to unseen environments, with an 18.2 percentage point gain on WorkArena L1 (an enterprise platform never seen during training) and consistent improvements across three additional benchmarks. Ablations confirm that each pipeline component contributes meaningfully, with Judge filtering, evaluation hints, and reasoning traces each accounting for measurable gains. These results demonstrate that structured trajectory synthesis from a single frontier teacher is sufficient to produce competitive, locally deployable web agents. Project page: https://agent-as-annotators.github.io