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arXiv:2604.17745v1 Announce Type: new Abstract: Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental results. However, existing approaches still use fixed sequential agent pipelines with weak global coordination, which limits their robustness and overall performance. In this work, we propose Hierarchical Research Agent System (HiRAS), a hierarchical multi-agent framework for end-to-end experiment reproduction that employs supervisory manager agents to coordinate specialised agents across fine-grained stages. We also identify limitations in the reference-free evaluation of the Paper2Code benchmark and introduce Paper2Code-Extra (P2C-Ex), a refined protocol that incorporates repository-level information and better aligns with the original reference-based metric. We conduct extensive evaluation, validating the effectiveness and robustness of our proposed methods, and observing improvements, including >10% relative performance gain beyond the previous state-of-the-art using open-source backbone models and significantly reduced hallucination in evaluation. Our work is available on GitHub: https://github.com/KOU-199024/HiRAS.

In this tutorial, we build an end-to-end implementation around Qwen 3.6-35B-A3B and explore how a modern multimodal MoE model can be used in practical workflows. We begin by setting up the environment, loading the model adaptively based on available GPU memory, and creating a reusable chat framework that supports both standard responses and explicit thinking traces. From there, we work through important capabilities such as thinking-budget control, streamed generation with separated reasoning and answers, vision input handling, tool calling, structured JSON generation, MoE routing inspection, benchmarking, retrieval-augmented generation, and session persistence. Through this process, we run the model for inference and also examine how to design a robust application layer on top of Qwen 3.6 for real experimentation and advanced prototyping. Copy CodeCopiedUse a different Browser import subprocess, sys def _pip(*a): subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, *a]) _pip(“–upgrade”, “pip”) _pip(“–upgrade”, “transformers>=4.48.0”, “accelerate>=1.2.0”, “bitsandbytes>=0.44.0”, “pillow”, “requests”, “sentencepiece”, “qwen-vl-utils[decord]”, “sentence-transformers”, “jsonschema”) import torch, os, json, time, re, gc, io, threading, textwrap, warnings from collections import Counter from typing import Any, Optional warnings.filterwarnings(“ignore”) assert torch.cuda.is_available(), “GPU required. Switch runtime to A100 / L4.” p = torch.cuda.get_device_properties(0) VRAM_GB = p.total_memory / 1e9 print(f”GPU: {p.name} | VRAM: {VRAM_GB:.1f} GB | CUDA {torch.version.cuda} | torch {torch.__version__}”) if VRAM_GB >= 75: LOAD_MODE = “bf16” elif VRAM_GB >= 40: LOAD_MODE = “int8” else: LOAD_MODE = “int4” try: import flash_attn ATTN_IMPL = “flash_attention_2” except Exception: ATTN_IMPL = “sdpa” print(f”-> mode={LOAD_MODE} attn={ATTN_IMPL}”) from transformers import ( AutoModelForImageTextToText, AutoProcessor, BitsAndBytesConfig, TextIteratorStreamer, StoppingCriteria, StoppingCriteriaList, ) MODEL_ID = “Qwen/Qwen3.6-35B-A3B” kwargs = dict(device_map=”auto”, trust_remote_code=True, low_cpu_mem_usage=True, attn_implementation=ATTN_IMPL, torch_dtype=torch.bfloat16) if LOAD_MODE == “int8”: kwargs[“quantization_config”] = BitsAndBytesConfig(load_in_8bit=True) elif LOAD_MODE == “int4”: kwargs[“quantization_config”] = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type=”nf4″, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True) print(“Loading processor…”) processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True) print(f”Loading model in {LOAD_MODE} (first run downloads ~70GB) …”) t0 = time.time() model = AutoModelForImageTextToText.from_pretrained(MODEL_ID, **kwargs); model.eval() print(f”Loaded in {time.time()-t0:.0f}s | VRAM used: {torch.cuda.memory_allocated()/1e9:.1f} GB”) SAMPLING = { “thinking_general”: dict(temperature=1.0, top_p=0.95, top_k=20, presence_penalty=1.5), “thinking_coding”: dict(temperature=0.6, top_p=0.95, top_k=20, presence_penalty=0.0), “instruct_general”: dict(temperature=0.7, top_p=0.80, top_k=20, presence_penalty=1.5), “instruct_reason”: dict(temperature=1.0, top_p=1.00, top_k=40, presence_penalty=2.0), } THINK_OPEN, THINK_CLOSE = “<think>”, “</think>” def split_thinking(text: str): if THINK_OPEN in text and THINK_CLOSE in text: a = text.index(THINK_OPEN) + len(THINK_OPEN); b = text.index(THINK_CLOSE) return text[a:b].strip(), text[b + len(THINK_CLOSE):].strip() if THINK_CLOSE in text: b = text.index(THINK_CLOSE) return text[:b].strip(), text[b + len(THINK_CLOSE):].strip() return “”, text.strip() We set up the full environment required to run Qwen 3.6-35B-A3B in Google Colab and installed all supporting libraries for quantization, multimodal processing, retrieval, and schema validation. We then probe the available GPU, dynamically select the loading mode based on VRAM, and configure the attention backend so the model runs as efficiently as possible on the given hardware. After that, we load the processor and model from Hugging Face and define the core sampling presets and the thinking-splitting utility, which lay the foundation for all later interactions. Copy CodeCopiedUse a different Browser class QwenChat: def __init__(self, model, processor, system=None, tools=None): self.model, self.processor = model, processor self.tokenizer = processor.tokenizer self.history: list[dict] = [] if system: self.history.append({“role”: “system”, “content”: system}) self.tools = tools def user(self, content): self.history.append({“role”:”user”,”content”:content}); return self def assistant(self, content, reasoning=””): m = {“role”:”assistant”,”content”:content} if reasoning: m[“reasoning_content”] = reasoning self.history.append(m); return self def tool_result(self, name, result): self.history.append({“role”:”tool”,”name”:name, “content”: result if isinstance(result, str) else json.dumps(result)}) return self def _inputs(self, enable_thinking, preserve_thinking): return self.processor.apply_chat_template( self.history, tools=self.tools, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors=”pt”, enable_thinking=enable_thinking, preserve_thinking=preserve_thinking, ).to(self.model.device) def generate(self, *, enable_thinking=True, preserve_thinking=False, max_new_tokens=2048, preset=”thinking_general”, stopping_criteria=None, append_to_history=True): inp = self._inputs(enable_thinking, preserve_thinking) cfg = SAMPLING[preset] gk = dict(**inp, max_new_tokens=max_new_tokens, do_sample=True, temperature=cfg[“temperature”], top_p=cfg[“top_p”], top_k=cfg[“top_k”], repetition_penalty=1.0, pad_token_id=self.tokenizer.pad_token_id or self.tokenizer.eos_token_id) if stopping_criteria is not None: gk[“stopping_criteria”] = stopping_criteria with torch.inference_mode(): out = self.model.generate(**gk) raw = self.tokenizer.decode(out[0, inp[“input_ids”].shape[-1]:], skip_special_tokens=True) think, ans = split_thinking(raw) if append_to_history: self.assistant(ans, reasoning=think) return think, ans def stream(self, *, enable_thinking=True, preserve_thinking=False, max_new_tokens=2048, preset=”thinking_general”, on_thinking=None, on_answer=None): inp = self._inputs(enable_thinking, preserve_thinking) cfg = SAMPLING[preset] streamer = TextIteratorStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True) gk = dict(**inp, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, temperature=cfg[“temperature”], top_p=cfg[“top_p”], top_k=cfg[“top_k”], pad_token_id=self.tokenizer.pad_token_id or self.tokenizer.eos_token_id) t = threading.Thread(target=self.model.generate, kwargs=gk); t.start() buf, in_think = “”, enable_thinking think_text, answer_text = “”, “” for piece in streamer: buf += piece if in_think: if THINK_CLOSE in buf: close_at = buf.index(THINK_CLOSE) resid = buf[:close_at] if on_thinking: on_thinking(resid[len(think_text):]) think_text = resid buf = buf[close_at + len(THINK_CLOSE):] in_think = False if buf and on_answer: on_answer(buf) answer_text = buf; buf = “” else: if on_thinking: on_thinking(piece) think_text += piece else: if on_answer: on_answer(piece) answer_text += piece t.join() self.assistant(answer_text.strip(), reasoning=think_text.strip()) return think_text.strip(), answer_text.strip() def save(self, path): with open(path, “w”) as f: json.dump({“history”: self.history, “tools”: self.tools}, f, indent=2) @classmethod def load(cls, model, processor, path): with open(path) as f: data = json.load(f) c = cls(model, processor, tools=data.get(“tools”)) c.history = data[“history”]; return c class ThinkingBudget(StoppingCriteria): def __init__(self, tokenizer, budget: int): self.budget = budget self.open_ids = tokenizer.encode(THINK_OPEN, add_special_tokens=False) self.close_ids = tokenizer.encode(THINK_CLOSE, add_special_tokens=False) self.start = None def _find(self, seq, needle): n = len(needle) for i in range(len(seq)-n+1): if seq[i:i+n] == needle: return i return None def __call__(self, input_ids, scores, **kwargs): seq = input_ids[0].tolist() if self.start is None: idx = self._find(seq, self.open_ids) if idx is not None: self.start = idx + len(self.open_ids) return False if self._find(seq[self.start:], self.close_ids) is not None: return False return (len(seq) – self.start) >= self.budget TOOL_CALL_RE = re.compile(r”<tool_call>s*({.*?})s*</tool_call>”, re.S) def run_calculate(expr: str) -> str: if any(c not in “0123456789+-*/().% ” for c in expr): return json.dumps({“error”:”illegal chars”}) try: return json.dumps({“result”: eval(expr, {“__builtins__”: {}}, {})}) except Exception as e: return json.dumps({“error”: str(e)}) _DOCS = { “qwen3.6”: “Qwen3.6-35B-A3B is a 35B MoE with 3B active params and 262k native context.”, “deltanet”: “Gated DeltaNet is a linear-attention variant used in Qwen3.6’s hybrid layers.”, “moe”: “Qwen3.6 uses 256 experts with 8 routed + 1 shared per token.”, } def run_search_docs(q): hits = [v for k,v in _DOCS.items() if k in q.lower()] return json.dumps({“results”: hits or [“no hits”]}) def run_get_time(): import datetime as dt return json.dumps({“iso”: dt.datetime.utcnow().isoformat()+”Z”}) TOOL_FNS = { “calculate”: lambda a: run_calculate(a[“expression”]), “search_docs”: lambda a: run_search_docs(a[“query”]), “get_time”: lambda a: run_get_time(), } TOOLS_SCHEMA = [ {“type”:”function”,”function”:{“name”:”calculate”,”description”:”Evaluate arithmetic.”, “parameters”:{“type”:”object”,”properties”:{“expression”:{“type”:”string”}},”required”:[“expression”]}}}, {“type”:”function”,”function”:{“name”:”search_docs”,”description”:”Search internal docs.”, “parameters”:{“type”:”object”,”properties”:{“query”:{“type”:”string”}},”required”:[“query”]}}}, {“type”:”function”,”function”:{“name”:”get_time”,”description”:”Get current UTC time.”, “parameters”:{“type”:”object”,”properties”:{}}}}, ] We build the main QwenChat conversation manager, which handles message history, tool messages, chat template formatting, standard generation, streaming generation, and session persistence. We also define the ThinkingBudget stopping criterion to

Tech workers in China are being instructed by their bosses to train AI agents to replace them—and it’s prompting a wave of soul-searching among otherwise enthusiastic early adopters.  Earlier this month a GitHub project called Colleague Skill, which claimed workers could use it to “distill” their colleagues’ skills and personality traits and replicate them with an AI agent, went viral on Chinese social media. Though the project was created as a spoof, it struck a nerve among tech workers, a number of whom told MIT Technology Review that their bosses are encouraging them to document their workflows in order to automate specific tasks and processes using AI agent tools like OpenClaw or Claude Code.  To set up Colleague Skill, a user names the coworker whose tasks they want to replicate and adds basic profile details. The tool then automatically imports chat history and files from Lark and DingTalk, both popular workplace apps in China, and generates reusable manuals describing that coworker’s duties—and even their unique quirks—for an AI agent to replicate.  Colleague Skill was created by Tianyi Zhou, who works as an engineer at the Shanghai Artificial Intelligence Laboratory. Earlier this week he told Chinese outlet Southern Metropolis Daily that the project was started as a stunt, prompted by AI-related layoffs and by the growing tendency of companies to ask employees to automate themselves. He didn’t respond to requests for further comment. Internet users have found humor in the idea behind the tool, joking about automating their coworkers before themselves. However, Colleague Skill’s virality has sparked a lot of debate about workers’ dignity and individuality in the age of AI. After seeing Colleague Skill on social media, Amber Li, 27, a tech worker in Shanghai, used it to recreate a former coworker as a personal experiment. Within minutes, the tool created a file detailing how that person did their job. “It is surprisingly good,” Li says. “It even captures the person’s little quirks, like how they react and their punctuation habits.” With this skill, Li can use an AI agent as a new “coworker” that helps debug her code and replies instantly. It felt uncanny and uncomfortable, Li says.  Even so,  replacing coworkers with agents could become a norm. Since OpenClaw became a national craze, bosses in China have been pushing tech workers to experiment with agents.  Although AI agents can take control of your computer, read and summarize news, reply to emails, and book restaurant reservations for you, tech workers on the ground say their utility has so far proven to be limited in business contexts. Asking employees to make manuals describing the minutiae of their day-to-day jobs the way Colleague Skill does is one way to help bridge that gap.  Hancheng Cao, an assistant professor at Emory University who studies AI and work, believes that companies have good reasons to push employees to create work blueprints like these, beyond simply following a trend. “Firms gain not only internal experience with the tools, but also richer data on employee know-how, workflows, and decision patterns. That helps companies see which parts of work can be standardized or codified into systems, and which still depend on human judgment,” he says. To employees, though, making agents or even blueprints for them can feel strange and alienating. One software engineer, who spoke with MIT Technology Review anonymously because of concerns about their job security, trained an AI (not Colleague Skill) on their workflow and found that the process felt reductive—as if their work had been flattened into modules in a way that made them easier to replace. On social media, workers have turned to bleak humor to express similar feelings. In one comment on Rednote, a user wrote that “a cold farewell can be turned into warm tokens,” quipping that if they use Colleague Skill to distill their coworkers into tasks first, they themselves might survive a little longer. The push for creating agents has also spurred clever countermeasures. Irritated by the idea of reducing a person to a skill, Koki Xu, 26 an AI product manager in Beijing, published an “anti-distillation” skill on GitHub on April 4. The tool, which took Xu about an hour to build, is designed to sabotage the process of creating workflows for agents. Users can choose between light, medium, and heavy sabotage modes depending on how closely their boss is observing the process, and the agent rewrites the material into generic, non-actionable language that would produce a less useful AI stand-in. A video Xu posted about the project went viral, drawing more than 5 million likes across platforms. Xu told MIT Technology Review that she has been following the Colleague Skill trend from the start and that it has made her think about alienation, disempowerment, and broader implications for labor. “I originally wanted to write an op-ed, but decided it would be more useful to make something that pushes back against it,” she says. Xu, who has undergraduate and master’s degrees in law, said the trend also raises legal questions. While a company may be able to argue that work chat histories and materials created on a work laptop are corporate property, a skill like this can also capture elements of personality, tone, and judgment, making ownership much less clear. She said she hopes Colleague Skill prompts more discussion about how to protect workers’ dignity and identity in the age of AI. “I believe it’s important to keep up with these trends so we (employees) can participate in shaping how they are used,” she says. Xu herself is an avid AI adopter, with seven OpenClaw agents set up across her personal and work devices. Li, the tech worker in Shanghai, says her company has not yet found a way to replace actual workers with AI tools, largely because they remain unreliable and require constant supervision. “I don’t feel like my job is immediately at risk,” she says. “But I do feel that my value is being cheapened, and I don’t know what to do about it.”

Cybersecurity has always had a dual-use problem: the same technical knowledge that helps defenders find vulnerabilities can also help attackers exploit them. For AI systems, that tension is sharper than ever. Restrictions intended to prevent harm have historically created friction for good-faith security work, and it can be genuinely difficult to tell whether any particular cyber action is intended for defensive usage or to cause harm. OpenAI is now proposing a concrete structural solution to that problem: verified identity, tiered access, and a purpose-built model for defenders. OpenAI team announced that it is scaling up its Trusted Access for Cyber (TAC) program to thousands of verified individual defenders and hundreds of teams responsible for defending critical software. The main focus of this expansion is the introduction of GPT-5.4-Cyber, a variant of GPT-5.4 fine-tuned specifically for defensive cybersecurity use cases. What Is GPT-5.4-Cyber and How Does It Differ From Standard Models? If you’re an AI engineer or data scientist who has worked with large language models on security tasks, you’re likely familiar with the frustrating experience of a model refusing to analyze a piece of malware or explain how a buffer overflow works — even in a clearly research-oriented context. GPT-5.4-Cyber is designed to eliminate that friction for verified users. Unlike standard GPT-5.4, which applies blanket refusals to many dual-use security queries, GPT-5.4-Cyber is described by OpenAI as ‘cyber-permissive’ — meaning it has a deliberately lower refusal threshold for prompts that serve a legitimate defensive purpose. That includes binary reverse engineering, enabling security professionals to analyze compiled software for malware potential, vulnerabilities, and security robustness without access to the source code. Binary reverse engineering without source code is a significant capability unlock. In practice, defenders routinely need to analyze closed-source binaries — firmware on embedded devices, third-party libraries, or suspected malware samples — without having access to the original code. That model was described as a GPT-5.4 variant purposely fine-tuned for additional cyber capabilities, with fewer capability restrictions and support for advanced defensive workflows including binary reverse engineering without source code. There are also hard limits. Users with trusted access must still abide by OpenAI’s Usage Policies and Terms of Use. The approach is designed to reduce friction for defenders while preventing prohibited behavior, including data exfiltration, malware creation or deployment, and destructive or unauthorized testing. This distinction matters: TAC lowers the refusal boundary for legitimate work, but does not suspend policy for any user. There are also deployment constraints. Use in zero-data-retention environments is limited, given that OpenAI has less visibility into the user, environment, and intent in those configurations — a tradeoff the company frames as a necessary control surface in a tiered-access model. For dev teams accustomed to running API calls in Zero-Data-Retention mode, this is an important implementation constraint to plan around before building pipelines on top of GPT-5.4-Cyber. The Tiered Access Framework: How TAC Actually Works TAC is not a checkbox feature — it is an identity-and-trust-based access framework with multiple tiers. Understanding the structure matters if you or your organization plans to integrate these capabilities. The access process runs through two paths. Individual users can verify their identity at chatgpt.com/cyber. Enterprises can request trusted access for their team through an OpenAI representative. Customers approved through either path gain access to model versions with reduced friction around safeguards that might otherwise trigger on dual-use cyber activity. Approved uses include security education, defensive programming, and responsible vulnerability research. TAC customers who want to go further and authenticate as cyber defenders can express interest in additional access tiers, including GPT-5.4-Cyber. Deployment of the more permissive model is starting with a limited, iterative rollout to vetted security vendors, organizations, and researchers. That means OpenAI is now drawing at least three practical lines instead of one: there is baseline access to general models; there is trusted access to existing models with less accidental friction for legitimate security work; and there is a higher tier of more permissive, more specialized access for vetted defenders who can justify it. The framework is grounded in three explicit principles. The first is democratized access: using objective criteria and methods, including strong KYC and identity verification, to determine who can access more advanced capabilities, with the goal of making those capabilities available to legitimate actors of all sizes, including those protecting critical infrastructure and public services. The second is iterative deployment — OpenAI updates models and safety systems as it learns more about the benefits and risks of specific versions, including improving resilience to jailbreaks and adversarial attacks. The third is ecosystem resilience, which includes targeted grants, contributions to open-source security initiatives, and tools like Codex Security. How the Safety Stack Is Built: From GPT-5.2 to GPT-5.4-Cyber It’s worth understanding how OpenAI has structured its safety architecture across model versions — because TAC is built on top of that architecture, not instead of it. OpenAI began cyber-specific safety training with GPT-5.2, then expanded it with additional safeguards through GPT-5.3-Codex and GPT-5.4. A critical milestone in that progression: GPT-5.3-Codex is the first model OpenAI is treating as High cybersecurity capability under its Preparedness Framework, which requires additional safeguards. These safeguards include training the model to refuse clearly malicious requests like stealing credentials. The Preparedness Framework is OpenAI’s internal evaluation rubric for classifying how dangerous a given capability level could be. Reaching ‘High’ under that framework is what triggered the full cybersecurity safety stack being deployed — not just model-level training, but an additional automated monitoring layer. In addition to safety training, automated classifier-based monitors detect signals of suspicious cyber activity and route high-risk traffic to a less cyber-capable model, GPT-5.2. In other words, if a request looks suspicious enough to exceed a threshold, the platform doesn’t just refuse — it silently reroutes the traffic to a safer fallback model. This is a key architectural detail: safety is enforced not only inside model weights, but also at the infrastructure routing layer. GPT-5.4-Cyber extends this stack further upward — more permissive for verified defenders, but wrapped in